Gerhard Hessler

Drug Design Strategies for Targeting G-Protein-Coupled Receptors†

G‐protein‐coupled receptors (GPCRs) form a large protein family that plays an important role in many physiological and pathophysiological processes. Since the sequencing of the human genome has revealed several hundred new members of this receptor family, many new opportunities for developing novel therapeutics have emerged. The increasing knowledge of GPCRs (biological target space) and their ligands (chemical ligand space) enables novel drug design strategies to accelerate the finding and optimization of GPCR leads: The crystal structure of rhodopsin provides the first three‐dimensional GPCR information, which now supports homology modeling studies and structure‐based drug design approaches within the GPCR target family. On the other hand, the classical ligand‐based design approaches (for example, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR)) are still powerful methods for lead finding and optimization. In addition, the cross‐target analysis of GPCR ligands has revealed more and more common structural motifs and three‐dimensional pharmacophores. Such GPCR privileged structural motifs have been successfully used by many pharmaceutical companies to design and synthesize combinatorial libraries, which are subsequently tested against novel GPCR targets for lead finding. In the near future structural biology and chemogenomics might allow the mapping of the ligand binding to the receptor. The linking of chemical and biological spaces will aid in generating lead‐finding libraries, which are tailor‐made for their respective receptor.

The scaffold hopping potential of pharmacophores

The goal of scaffold hopping is to replace the chemical core structure by a novel chemical motif while keeping the biological activity of the molecule. As pharmacophores define chemical features essential for biological activity, they can be successfully employed to guide scaffold replacements. To this end, various novel approaches have recently been developed and applied.

Identification of High-Affinity P2Y12 Antagonists Based on a Phenylpyrazole Glutamic Acid Piperazine Backbone

A series of novel, highly potent P2Y₁₂ antagonists as inhibitors of platelet aggregation based on a phenylpyrazole glutamic acid piperazine backbone is described. Exploration of the structural requirements of the substituents by probing the structure-activity relationship along this backbone led to the discovery of the N-acetyl-(S)-proline cyclobutyl amide moiety as a highly privileged motif. Combining the most favorable substituents led to remarkably potent P2Y₁₂ antagonists displaying not only low nanomolar binding affinity to the P2Y₁₂ receptor but also a low nanomolar inhibition of platelet aggregation in the human platelet rich plasma assay with IC₅₀ values below 50 nM. Using a homology and a three-dimensional quantitative structure-activity relationship model, a binding hypothesis elucidating the impact of several structural features was developed.

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

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

Fast similarity searching and screening hit analysis

Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.:

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

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

Predictive in silico off-target profiling in drug discovery

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

Fractal Dimensions of Macromolecular Structures.

Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less‐explored geometric features of macromolecules is molecular surface irregularity, or ‘roughness’, which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large‐scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand‐binding pockets and protein‐protein interfaces, exhibit greater local fluctuations in their fractal dimensions than ‘inert’ surface areas. We expect approximately 22 % of a protein’s surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.

Phototoxicity – from molecular descriptors to classification models

Potential photoactivation of certain pharmaceuticals, cosmetic ingredients and natural products by sunlight (e.g., phenothiazines, arylsulfonamides, or coumarins) has to be considered early on in development in order to avoid serious adverse effects (for example phototoxic or photoallergic reactions). Current clinical trial registration guidelines (FDA May 2003 [1], EMEA Dec. 2002 [2]) recommend photosafety testing of molecules if they exhibit strong absorption bands between 290-700nm and if they are significantly partitioned in human skin or eyes. The UV absorption coefficients and the tissue partitioning of a compound are considered as important factors for phototoxic effects. However, the rationalization and prediction of phototoxicity by (quantitative) structure-property relationships ((Q)SPR) offers a valuable strategy to reduce experimental testing if an appropriate precision level of the underlying model is guaranteed. A diverse data set of known phototoxicants and non-phototoxicants including various molecular chemotypes (90 % of them are pharmaceuticals) was compiled. After geometry optimization the maximum absorption wavelength of each compound was calculated by semi-empirical methods followed by subsequent computation of molecular descriptors. Our insilico analysis (e.g., PLS and recursive partitioning) of quantum chemical as well as classical molecular descriptors (e.g., LUMO, HOMO/LUMO gap, electron affinity, ionization energy, molecular fragments, physicochemical descriptors such as logD, pKa and logPeff) has led to predictive photosafety classifiers. Model validation was performed with a proprietary external test set of an in vitro photosafety assay (3T3 neutral red assay). Our photosafety models are currently applied in a prospective manner in the prioritization, classification and labeling of newly designed molecules.

Identification and Application of Antitarget Activity Hotspots to Guide Compound Optimization

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