Edgar Jacoby

The 7 TM G-protein-coupled receptor target family

Chemical biology approaches have a long history in the exploration of the G‐protein‐coupled receptor (GPCR) family, which represents the largest and most important group of targets for therapeutics. The analysis of the human genome revealed a significant number of new members with unknown physiological function which are today the focus of many reverse pharmacology drug‐discovery programs. As the seven hydrophobic transmembrane segments are a defining common structural feature of these receptors, and as signaling through heterotrimeric G proteins is not demonstrated in all cases, these proteins are also referred to as seven transmembrane (7 TM) or serpentine receptors. This review summarizes important historic milestones of GPCR research, from the beginning, when pharmacology was mainly descriptive, to the age of modern molecular biology, with the cloning of the first receptor and now the availability of the entire human GPCR repertoire at the sequence and protein level. It shows how GPCR‐directed drug discovery was initially based on the careful testing of a few specifically made chemical compounds and is today pursued with modern drug‐discovery approaches, including combinatorial library design, structural biology, molecular informatics, and advanced screening technologies for the identification of new compounds that activate or inhibit GPCRs specifically. Such compounds, in conjunction with other new technologies, allow us to study the role of receptors in physiology and medicine, and will hopefully result in novel therapies. We also outline how basic research on the signaling and regulatory mechanisms of GPCRs is advancing, leading to the discovery of new GPCR‐interacting proteins and thus opening new perspectives for drug development. Practical examples from GPCR expression studies, HTS (high‐throughput screening), and the design of monoamine‐related GPCR‐focused combinatorial libraries illustrate ongoing GPCR chemical biology research. Finally, we outline future progress that may relate today’s discoveries to the development of new medicines.

CHEMOGENOMICS: AN EMERGING STRATEGY FOR RAPID TARGET AND DRUG DISCOVERY

Chemogenomics is an emerging discipline that combines the latest tools of genomics and chemistry and applies them to target and drug discovery. Its strength lies in eliminating the bottleneck that currently occurs in target identification by measuring the broad, conditional effects of chemical libraries on whole biological systems or by screening large chemical libraries quickly and efficiently against selected targets. The hope is that chemogenomics will concurrently identify and validate therapeutic targets and detect drug candidates to rapidly and effectively generate new treatments for many human diseases.

Comparison of fingerprint-based methods for virtual screening using multiple bioactive reference structures

Fingerprint-based similarity searching is widely used for virtual screening when only a single bioactive reference structure is available. This paper reviews three distinct ways of carrying out such searches when multiple bioactive reference structures are available: merging the individual fingerprints into a single combined fingerprint; applying data fusion to the similarity rankings resulting from individual similarity searches; and approximations to substructural analysis. Extended searches on the MDL Drug Data Report database suggest that fusing similarity scores is the most effective general approach, with the best individual results coming from the binary kernel discrimination technique.

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes.

Library Design for Fragment Based Screening

According to Hanns model of molecular complexity an increased probability of detection binding to a target protein can be expected when small, low complex molecular fragments are screened with high sensitivity instead of full-sized ligands with lower sensitivity. Analysis of the HTS summary data of Novartis and comparison with NMR screening results obtained on generic fragment libraries indicate this expectation to be true with hitrates of 0.001% - 0.151% observed in the identification of ligands with an IC(50) threshold in the micromolar range in an HTS setup and hitrates above or equal to 3% observed in NMR screening of fragments with an affinity threshold in the millimolar range. It is however necessary to keep in mind that the sets of target studied were not identical for both method and the experience in NMR screening is too limited for a final conclusion. The term hitrate as used here reflects only the success rate in the observation of ligand binding event. It must not be confused with the overall success rate of fragment and high throughput screening in the lead finding process, which can be entirely different, since the steps required to follow-up a ligand binding event to a lead are different for both methods. A survey of fragment-based lead discovery case studies given in the literature shows that in approximately half of the cases the initial hit fragment was discovered by screening a generic library, whereas in the other cases some knowledge about an initial ligands or the protein binding site has been used, whereas systematic virtual screening of fragment databases has been only rarely reported. As comparatively high hitrates were obtained, further consideration to optimize the generic fragment screening library were directed to the chemical tractability of the fragment. As several functional groups preferred by chemists for modification and linking of the fragments are also preferentially involved in interactions between the fragments and the target protein, a set of screening fragments was derived from chemical building blocks by masking its linker group by a chemical transformation which can be later on used in the chemical follow-up of the fragment hit. For example primary amines can be masked as acetamides. If the screening fragment is active the related building block can then be used for synthesis of a follow-up library.

Modeling Promiscuity Based on in vitro Safety Pharmacology Profiling Data

This study describes a method for mining and modeling binding data obtained from a large panel of targets (in vitro safety pharmacology) to distinguish differences between promiscuous and selective compounds. Two naïve Bayes models for promiscuity and selectivity were generated and validated on a test set as well as publicly available drug databases. The model shows a higher score (lower promiscuity) for marketed drugs than for compounds in early development or compounds that failed during clinical development. Such models can be used in triaging high‐throughput screening data or for lead optimization.

New Methods for Ligand-Based Virtual Screening: Use of Data Fusion and Machine Learning to Enhance the Effectiveness of Similarity Searching

Similarity searching using a single bioactive reference structure is a well-established technique for accessing chemical structure databases. This paper describes two extensions of the basic approach. First, we discuss the use of group fusion to combine the results of similarity searches when multiple reference structures are available. We demonstrate that this technique is notably more effective than conventional similarity searching in scaffold-hopping searches for structurally diverse sets of active molecules; conversely, the technique will do little to improve the search performance if the actives are structurally homogeneous. Second, we make the assumption that the nearest neighbors resulting from a similarity search, using a single bioactive reference structure, are also active and use this assumption to implement approximate forms of group fusion, substructural analysis, and binary kernel discrimination. This approach, called turbo similarity searching, is notably more effective than conventional similarity searching.

A small-molecule dengue virus entry inhibitor.

ABSTRACT The incidence of dengue fever epidemics has increased dramatically over the last few decades. However, no vaccine or antiviral therapies are available. Therefore, the need for safe and effective antiviral drugs has become imperative. The entry of dengue virus into a host cell is mediated by its major envelope (E) protein. The crystal structure of the E protein reveals a hydrophobic pocket that is presumably important for low-pH-mediated membrane fusion. High-throughput docking with this hydrophobic pocket was performed, and hits were evaluated in cell-based assays. Compound 6 was identified as one of the inhibitors and had an average 50% effective concentration of 119 nM against dengue virus serotype 2 in a human cell line. Mechanism-of-action studies demonstrated that compound 6 acts at an early stage during dengue virus infection. It arrests dengue virus in vesicles that colocalize with endocytosed dextran and inhibits NS3 expression. The inhibitors described in this report can serve as molecular probes for the study of the entry of flavivirus into host cells.

On scaffolds and hopping in medicinal chemistry.

The molecular scaffold is an oft-cited concept in medicinal chemistry suggesting that the definition of what makes a scaffold is rigorous and objective. However, this is far from the case with the definition of a scaffold being highly dependent on the particular viewpoint of a given scientist. It follows, therefore, that the definition of scaffold hopping and, more importantly, the detection of what constitutes a scaffold hop, is also ill-defined and highly subjective. Essentially, it is agreed that scaffolds should be substantially different from each other, although significantly similar to each other, to constitute a hop. In the latter, the scaffolds must permit a similar geometric arrangement of functional groups to permit the mode of action. However, this leaves the paradox of how to describe both scaffold similarity and dissimilarity simultaneously. In this paper, the current statuses of scaffolds and scaffold hopping are reviewed based on published examples of scaffold hopping from the literature. An investigation of the degree to which it is possible to formulate a more rigorous definition of scaffolds and hopping in the context of molecular topologies is considered. These techniques are adapted from chemoinformatics to be applied in the design of new medicinal compounds.

An Ontology for Pharmaceutical Ligands and Its Application for in Silico Screening and Library Design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.