Addressing Selective Polypharmacology of Antipsychotic Drugs Targeting the Bioaminergic Receptors through the Receptor Dynamic Conformational Ensembles

Selective polypharmacology, where a drug acts on multiple rather than single molecular targets involved in a disease, emerges to develop a structure-based system biology approach to design drugs selectively targeting a disease-active protein network. We focus on the bioaminergic receptors that belong to the group of integral membrane signalling proteins coupled to the G protein and represent targets for therapeutic agents against schizophrenia and depression. Among them, it has been shown that the serotonin (5-HT2A and 5-HT6), dopamine (D2 and D3) receptors induce a cognition-enhancing effect (group 1), while the histamine (H1) and serotonin (5-HT2C) receptors lead to metabolic side effects and the 5-HT2B serotonin receptor causes pulmonary hypertension (group 2). Thus, the problem arises to develop an approach that allows identifying drugs targeting only the disease-active receptors, i.e. group 1. The recent release of several crystal structures of the bioaminergic receptors, involving the D3 and H1 receptors provides the possibility to model the structures of all receptors and initiate a study of the structural and dynamic context of selective polypharmacology. In this work, we use molecular dynamics simulations to generate a conformational space of the receptors and subsequently characterize its binding properties applying molecular probe mapping. All-against-all comparison of the generated probe maps of the selected diverse conformations of all receptors with the Tanimoto similarity coefficient (Tc) enable to separate the receptors of group 1 from group 2. The pharmacophore built based on the Tc-selected receptor conformations, using the multiple probe maps discovers structural features that can be used to design molecules selective towards the receptors of group 1. The importance of several predicted residues to ligand selectivity is supported by the available mutagenesis and ligand structure-activity relationships studies. In addition, the Tc-selected conformations of the receptors for group 1 show good performance in isolation of known ligands from a random decoy. Our computational structure-based protocol to tackle selective polypharmacology of antipsychotic drugs could be applied for other diseases involving multiple drug targets, such as oncologic and infectious disorders.

Functionalization of β-caryophyllene generates novel polypharmacology in the endocannabinoid system

The widespread dietary plant sesquiterpene hydrocarbon β-caryophyllene (1) is a CB2 cannabinoid receptor-specific agonist showing anti-inflammatory and analgesic effects in vivo. Structural insights into the pharmacophore of this hydrocarbon, which lacks functional groups other than double bonds, are missing. A structure-activity study provided evidence for the existence of a well-defined sesquiterpene hydrocarbon binding site in CB2 receptors, highlighting its exquisite sensitivity to modifications of the strained endocyclic double bond of 1. While most changes on this element were detrimental for activity, ring-opening cross metathesis of 1 with ethyl acrylate followed by amide functionalization generated a series of new monocyclic amides (11a, 11b, 11c) that not only retained the CB2 receptor functional agonism of 1 but also reversibly inhibited fatty acid amide hydrolase (FAAH), the major endocannabinoid degrading enzyme, without affecting monoacylglycerol lipase (MAGL) and α,β hydrolases 6 and 12. Intriguingly, further modification of this monocyclic scaffold generated the FAAH- and endocannabinoid substrate-specific cyclooxygenase-2 (COX-2) dual inhibitors 11e and 11f, which are probes with a novel pharmacological profile. Our study shows that by removing the conformational constraints induced by the medium-sized ring and by introducing functional groups in the sesquiterpene hydrocarbon 1, a new scaffold with pronounced polypharmacological features within the endocannabinoid system could be generated. The structural and functional repertoire of cannabimimetics and their yet poorly understood intrinsic promiscuity may be exploited to generate novel probes and ultimately more effective drugs.

Network approaches to drug discovery

Introduction: Advances in genomics technologies are providing a very large amount of data on genome-wide gene expression profiles, protein molecules and their interactions with other macromolecules and metabolites. Molecular interaction networks provide a useful way to capture this complex data and comprehend it. Networks are beginning to be used in drug discovery, in many steps of the modern discovery pipeline, with large-scale molecular networks being particularly useful for the understanding of the molecular basis of the disease. Areas covered: The authors discuss network approaches used for drug target discovery and lead identification in the drug discovery pipeline. By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance. Furthermore, the authors also look at networks which address particular important aspects such as off-target effects, combination-targets, mechanism of drug action and drug safety. Expert opinion: The network approach represents another paradigm shift in drug discovery science. A network approach provides a fresh perspective of understanding important proteins in the context of their cellular environments, providing a rational basis for deriving useful strategies in drug design. Besides drug target identification and inferring mechanism of action, networks enable us to address new ideas that could prove to be extremely useful for new drug discovery, such as drug repositioning, drug synergy, polypharmacology and personalized medicine.

Characterizing the pocketome of Mycobacterium tuberculosis and application in rationalizing polypharmacological target selection

Polypharmacology is beginning to emerge as an important concept in the field of drug discovery. However, there are no established approaches to either select appropriate target sets or design polypharmacological drugs. Here, we propose a structural-proteomics approach that utilizes the structural information of the binding sites at a genome-scale obtained through in-house algorithms to characterize the pocketome, yielding a list of ligands that can participate in various biochemical events in the mycobacterial cell. The pocket-type space is seen to be much larger than the sequence or fold-space, suggesting that variations at the site-level contribute significantly to functional repertoire of the organism. All-pair comparisons of binding sites within Mycobacterium tuberculosis (Mtb), pocket-similarity network construction and clustering result in identification of binding-site sets, each containing a group of similar binding sites, theoretically having a potential to interact with a common set of compounds. A polypharmacology index is formulated to rank targets by incorporating a measure of druggability and similarity to other pockets within the proteome. This study presents a rational approach to identify targets with polypharmacological potential along with possible drugs for repurposing, while simultaneously, obtaining clues on lead compounds for use in new drug-discovery pipelines.

Botanical drugs, synergy, and network pharmacology: forth and back to intelligent mixtures

For centuries the science of pharmacognosy has dominated rational drug development until it was gradually substituted by target-based drug discovery in the last fifty years. Pharmacognosy stems from the different systems of traditional herbal medicine and its "reverse pharmacology" approach has led to the discovery of numerous pharmacologically active molecules and drug leads for humankind. But do botanical drugs also provide effective mixtures? Nature has evolved distinct strategies to modulate biological processes, either by selectively targeting biological macromolecules or by creating molecular promiscuity or polypharmacology (one molecule binds to different targets). Widely claimed to be superior over monosubstances, mixtures of bioactive compounds in botanical drugs allegedly exert synergistic therapeutic effects. Despite evolutionary clues to molecular synergism in nature, sound experimental data are still widely lacking to support this assumption. In this short review, the emerging concept of network pharmacology is highlighted, and the importance of studying ligand-target networks for botanical drugs is emphasized. Furthermore, problems associated with studying mixtures of molecules with distinctly different pharmacodynamic properties are addressed. It is concluded that a better understanding of the polypharmacology and potential network pharmacology of botanical drugs is fundamental in the ongoing rationalization of phytotherapy.

Identification of endocannabinoid system-modulating N-alkylamides from Heliopsis helianthoides var. scabra and Lepidium meyenii.

The discovery of the interaction of plant-derived N-alkylamides (NAAs) and the mammalian endocannabinoid system (ECS) and the existence of a plant endogenous N-acylethanolamine signaling system have led to the re-evaluation of this group of compounds. Herein, the isolation of seven NAAs and the assessment of their effects on major protein targets in the ECS network are reported. Four NAAs, octadeca-2E,4E,8E,10Z,14Z-pentaene-12-ynoic acid isobutylamide (1), octadeca-2E,4E,8E,10Z,14Z-pentaene-12-ynoic acid 2'-methylbutylamide (2), hexadeca-2E,4E,9Z-triene-12,14-diynoic acid isobutylamide (3), and hexadeca-2E,4E,9,12-tetraenoic acid 2'-methylbutylamide (4), were identified from Heliopsis helianthoides var. scabra. Compounds 2-4 are new natural products, while 1 was isolated for the first time from this species. The previously described macamides, N-(3-methoxybenzyl)-(9Z,12Z,15Z)-octadecatrienamide (5), N-benzyl-(9Z,12Z,15Z)-octadecatrienamide (6), and N-benzyl-(9Z,12Z)-octadecadienamide (7), were isolated from Lepidium meyenii (Maca). N-Methylbutylamide 4 and N-benzylamide 7 showed submicromolar and selective binding affinities for the cannabinoid CB1 receptor (Ki values of 0.31 and 0.48 μM, respectively). Notably, compound 7 also exhibited weak fatty acid amide hydrolase (FAAH) inhibition (IC50 = 4 μM) and a potent inhibition of anandamide cellular uptake (IC50 = 0.67 μM) that was stronger than the inhibition obtained with the controls OMDM-2 and UCM707. The pronounced ECS polypharmacology of compound 7 highlights the potential involvement of the arachidonoyl-mimicking 9Z,12Z double-bond system in the linoleoyl group for the overall cannabimimetic action of NAAs. This study provides additional strong evidence of the endocannabinoid substrate mimicking of plant-derived NAAs and uncovers a direct and indirect cannabimimetic action of the Peruvian Maca root.

4'-O-methylhonokiol increases levels of 2-arachidonoyl glycerol in mouse brain via selective inhibition of its COX-2-mediated oxygenation

BACKGROUND AND PURPOSE 4'-O-methylhonokiol (MH) is a natural product showing anti-inflammatory, anti-osteoclastogenic, and neuroprotective effects. MH was reported to modulate cannabinoid CB2 receptors as an inverse agonist for cAMP production and an agonist for intracellular [Ca2+]. It was recently shown that MH inhibits cAMP formation via CB2 receptors. In this study, the exact modulation of MH on CB2 receptor activity was elucidated and its endocannabinoid substrate-specific inhibition (SSI) of cyclooxygenase-2 (COX-2) and CNS bioavailability are described for the first time. METHODS CB2 receptor modulation ([35S]GTPγS, cAMP, and β-arrestin) by MH was measured in hCB2-transfected CHO-K1 cells and native conditions (HL60 cells and mouse spleen). The COX-2 SSI was investigated in RAW264.7 cells and in Swiss albino mice by targeted metabolomics using LC-MS/MS. RESULTS MH is a CB2 receptor agonist and a potent COX-2 SSI. It induced partial agonism in both the [35S]GTPγS binding and β-arrestin recruitment assays while being a full agonist in the cAMP pathway. MH selectively inhibited PGE2 glycerol ester formation (over PGE2) in RAW264.7 cells and significantly increased the levels of 2-AG in mouse brain in a dose-dependent manner (3 to 20 mg kg(-1)) without affecting other metabolites. After 7 h from intraperitoneal (i.p.) injection, MH was quantified in significant amounts in the brain (corresponding to 200 to 300 nM). CONCLUSIONS LC-MS/MS quantification shows that MH is bioavailable to the brain and under condition of inflammation exerts significant indirect effects on 2-AG levels. The biphenyl scaffold might serve as valuable source of dual CB2 receptor modulators and COX-2 SSIs as demonstrated by additional MH analogs that show similar effects. The combination of CB2 agonism and COX-2 SSI offers a yet unexplored polypharmacology with expected synergistic effects in neuroinflammatory diseases, thus providing a rationale for the diverse neuroprotective effects reported for MH in animal models.

Développement et applications d’un outil bio-informatique pour la détection de similarités de champs d’interaction moléculaire

Résumé : Les méthodes de détection de similarités de sites de liaison servent entre autres à la prédiction de fonction et à la prédiction de cibles croisées. Ces méthodes peuvent aider à prévenir les effets secondaires, suggérer le repositionnement de médicament existants, identifier des cibles polypharmacologiques et des remplacements bio-isostériques. La plupart des méthodes utilisent des représentations basées sur les atomes, même si les champs d’interaction moléculaire (MIFs) représentent plus directement ce qui cherche à être identifié. Nous avons développé une méthode bio-informatique, IsoMif, qui détecte les similarités de MIF entre différents sites de liaisons et qui ne nécessite aucun alignement de séquence ou de structure. Sa performance a été comparée à d’autres méthodes avec des bancs d’essais, ce qui n’a jamais été fait pour une méthode basée sur les MIFs. IsoMif performe mieux en moyenne et est plus robuste. Nous avons noté des limites intrinsèques à la méthodologie et d’autres qui proviennent de la nature. L’impact de choix de conception sur la performance est discuté. Nous avons développé une interface en ligne qui permet la détection de similarités entre une protéine et différents ensembles de MIFs précalculés ou à des MIFs choisis par l’utilisateur. Des sessions PyMOL peuvent être téléchargées afin de visualiser les similarités identifiées pour différentes interactions intermoléculaires. Nous avons appliqué IsoMif pour identifier des cibles croisées potentielles de drogues lors d’une analyse à large échelle (5,6 millions de comparaisons). Des simulations d’arrimage moléculaire ont également été effectuées pour les prédictions significatives. L’objectif est de générer des hypothèses de repositionnement et de mécanismes d’effets secondaires observés. Plusieurs exemples sont présentés à cet égard.