Biological Characterization, Mechanistic Investigation and Structure-Activity Relationships of Chemically Stable TLR2 Antagonists.

Toll-like receptors (TLRs) build the first barrier in the innate immune response and therefore represent promising targets for the modulation of inflammatory processes. Recently, the pyrogallol-containing TLR2 antagonists CU-CPT22 and MMG-11 were reported; however, their 1,2,3-triphenol motif renders them highly susceptible to oxidation and excludes them from use in extended experiments under aerobic conditions. Therefore, we have developed a set of novel TLR2 antagonists (1-9) based on the systematic variation of substructures, linker elements, and the hydrogen-bonding pattern of the pyrogallol precursors by using chemically robust building blocks. The novel series of chemically stable and synthetically accessible TLR2 antagonists (1-9) was pharmacologically characterized, and the potential binding modes of the active compounds were evaluated structurally. Our results provide new insights into structure-activity relationships and allow rationalization of structural binding characteristics. Moreover, they support the hypothesis that this class of TLR ligands bind solely to TLR2 and do not directly interact with TLR1 or TLR6 of the functional heterodimer. The most active compound from this series (6), is chemically stable, nontoxic, TLR2-selective, and shows a similar activity with regard to the pyrogallol starting points, thus indicating the variability of the hydrogen bonding pattern.

Identification and validation of a novel dual small-molecule TLR2/8 antagonist.

Toll-like receptor 2 (TLR2) and TLR8 are involved in the recognition of bacterial and viral components and are linked not only to protective antimicrobial immunity but also to inflammatory diseases. Recently, increasing attention has been paid to the receptor crosstalk between TLR2 and TLR8 to fine-tune innate immune responses. In this study, we report a novel dual TLR2/TLR8 antagonist, compound 24 that was developed by a modeling-guided synthesis approach. The modulator was optimized from the previously reported 1,3-benzothiazole derivative, compound 8. Compound 24 was pharmacologically characterized for the ability to inhibit TLR2- and TLR8-mediated responses in TLR-overexpressing reporter cells and THP-1 macrophages. The modulator showed high efficacy with IC50 values in the low micromolar range for both TLRs, selectivity towards other TLRs and low cytotoxicity. At TLR2, a slight predominance for the TLR2/1 heterodimer was found in reporter cells selectively expressing TLR2/1 or TLR2/6 heterodimers. Concentration ratio analysis in the presence of Pam3CSK4 or Pam2CSK4 indicated non-competitive antagonist behavior at hTLR2. In computational docking studies, a plausible alternative binding mode of compound 24 was predicted for both TLR2 and TLR8. Our results provide evidence that it is feasible to simultaneously and selectively target endosomal- and surface-located TLRs. We identified a small-molecule dual TLR2/8 antagonist that may serve as a valuable pharmacological tool to decipher the role of TLR2/8 co-signaling in inflammation.

The novel small-molecule antagonist MMG-11 preferentially inhibits TLR2/1 signaling.

Toll-like receptor 2 (TLR2) forms heterodimers with either TLR1 or TLR6 to induce protective early inflammatory responses to pathogen- and damage-associated molecular patterns. However, excessive activation is associated with inflammatory and metabolic diseases. Several TLR2 antagonists have been described but pharmacological characterization is still at an early stage. Previously, we identified the potent and selective TLR2 antagonist MMG-11 by computational modelling and experimental validation. Here, we characterized the TLR2 antagonists MMG-11 and CU-CPT22 as well as the TIR-domain binding TLR2 antagonist C29 in TLR-overexpressing promoter cells as well as human and mouse macrophages. In line with our recent studies, MMG-11 abrogated pro-inflammatory cytokine secretion and NF-κB activation induced by different bacterial TLR2 agonists. MMG-11 preferentially inhibited TLR2/1 signaling in promoter cells stably expressing TLR2 heterodimers and mouse macrophages. Furthermore, the TLR2 antagonist blocked ligand-induced interaction of TLR2 with MyD88 and reduced MAP kinase and NF-κB activation. MMG-11 and CU-CPT22 but not C29 displaced Pam3CSK4 in an indirect binding assay confirming the competitive mode of action of MMG-11 and CU-CPT22. Isobologram analysis revealed additive and synergistic effects when the non-competitive antagonist C29 was combined with the competitive antagonist MMG-11 or CU-CPT22, respectively. In conclusion, we provide evidence that MMG-11 acts as a competitive antagonist with a predominance for the TLR2/1 heterodimer in human and mouse cells. Our results also indicate that MMG-11 is a model compound for studying TLR2 signaling.

Identification and characterization of a novel chemotype for human TLR8 inhibitors.

The endosomal Toll-like receptor 8 (TLR8) recognizes single-stranded RNA and initiates early inflammatory responses. Despite the importance of endosomal TLRs for human host defense against microbial pathogens, extensive activation may contribute to autoimmune and inflammatory diseases. In contrast to the recent progress made in the development of modulators of plasma membrane-bound TLRs, little is known about endosomal TLR modulation and very few TLR8 inhibitors have been reported. In this study, we discovered and validated novel small-molecule TLR8 inhibitors. Fourteen potential TLR8 modulators were experimentally validated in HEK293T cells stably overexpressing human TLR8 and THP-1 macrophages. Five compounds inhibited TLR8-mediated signaling, representing a hit rate of 36%. The three most potent compounds neither cause cellular toxicity nor inhibition of TLR signaling induced by other receptor subtypes. Conclusively, we experimentally confirm novel and selective, pyrimidine-based TLR8 inhibitors with low cytotoxicity that are relevant candidates for lead optimization and further mechanistic studies.

N-methyl-D-aspartate receptor dysfunction by unmutated human antibodies against the NR1 subunit.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is the most common autoimmune encephalitis related to autoantibody-mediated synaptic dysfunction. Cerebrospinal fluid-derived human monoclonal NR1 autoantibodies showed low numbers of somatic hypermutations or were unmutated. These unexpected germline-configured antibodies showed weaker binding to the NMDAR than matured antibodies from the same patient. In primary hippocampal neurons, germline NR1 autoantibodies strongly and specifically reduced total and synaptic NMDAR currents in a dose- and time-dependent manner. The findings suggest that functional NMDAR antibodies are part of the human naïve B cell repertoire. Given their effects on synaptic function, they might contribute to a broad spectrum of neuropsychiatric symptoms. Ann Neurol 2019;85:771-776.

Identification of a pyrogallol derivative as a potent and selective human TLR2 antagonist by structure-based virtual screening.

Toll-like receptor 2 (TLR2) induces early inflammatory responses to pathogen and damage-associated molecular patterns trough heterodimerization with either TLR1 or TLR6. Since overstimulation of TLR2 signaling is linked to several inflammatory and metabolic diseases, TLR2 antagonists may provide therapeutic benefits for the control of inflammatory conditions. We present virtual screening for the identification of novel TLR2 modulators, which combines analyses of known ligand sets with structure-based approaches. The 13 identified compounds were pharmacologically characterized in HEK293-hTLR2 cells, THP-1 macrophages and peripheral blood mononuclear cells for their ability to inhibit TLR2-mediated responses. Four out of 13 selected compounds show concentration-dependent activity, representing a hit rate of 31%. The most active compound is the pyrogallol derivative MMG-11 that inhibits both TLR2/1 and TLR2/6 signaling and shows a higher potency than the previously discovered CU-CPT22. Concentration ratio analysis identified both compounds as competitive antagonists of Pam3CSK4- and Pam2CSK4-induced responses. Schild plot analysis yielded apparent pA2 values of 5.73 and 6.15 (TLR2/1), and 5.80 and 6.65 (TLR2/6) for CU-CPT22 and MMG-11, respectively. MMG-11 neither shows cellular toxicity nor interference with signaling induced by other TLR agonists, IL-1ß or TNF. Taken together, we demonstrate that MMG-11 is a potent and selective TLR2 antagonist with low cytotoxicity rendering it a promising pharmacological tool for the investigation of TLR signaling and a suitable lead structure for further chemical optimization.

Balancing Inflammation: Computational Design of Small-Molecule Toll-like Receptor Modulators.

As essential proteins of the innate immune system, Toll-like receptors (TLRs) are involved in a plethora of physiological pathologies and their modulation is an ongoing quest in the field of drug discovery. Although TLRs recognize an unusually broad range of different molecular patterns, only a few small-molecule TLR modulators have been reported to date. Recent advances in crystallography and in silico techniques provide promising opportunities for TLR investigations and drug design. Here, three application areas for computational approaches are considered: (i) exploration of TLR structure and activation; (ii) understanding TLR modulation; and (iii) TLR drug discovery. By providing an overview on state-of-the-art computational methods, we highlight the value of molecular modeling in mechanistically understanding TLR function and guiding drug design.

New telmisartan-derived PPARγ agonists: Impact of the 3D-binding mode on the pharmacological profile.

In previous studies, the 4'-((2-propyl-1H-benzo[d]imidazol-1-yl)methyl)-[1,1'-biphenyl]-2-carboxylic acid was identified as pharmacophoric core for PPARγ activation. In this structure-activity relationship study the C2-alkyl chain was elongated and the 2-COOH group was changed to a carbamide/carbonitrile or shifted to the 3- or 4-position. Furthermore, the benzo[d]imidazole was exchanged by 2,3-dihydrobenzo[d]thiazole or 1H-indole. C2-propyl derivatives showed the profile of partial agonists, while elongation of the C2-chain to that of an n-heptyl group or a 4-COOH shift changed the pharmacological profile to that of a potent full agonist. This finding can be explained by binding to the LBD in different ligand conformations. Two anchoring points (Tyr473 and Arg288) exist in the LBD, which have to be contacted to achieve receptor activation. In a crystal violet chemosensitivity assay using COS-7 cells and LNCaP cells expressing PPARγ only the carbamide derivatives influenced the cell growth, independently on the presence of the PPARγ. Therefore, receptor mediated cytotoxicity can be excluded.

Acute myeloid leukaemia-derived Langerhans-like cells enhance Th1 polarization upon TLR2 engagement.

Langerhans cells (LCs) represent a highly specialized subset of epidermal dendritic cells (DCs), yet not fully understood in their function of balancing skin immunity. Here, we investigated in vitro generated Langerhans-like cells obtained from the human acute myeloid leukaemia cell line MUTZ-3 (MUTZ-LCs) to study TLR- and cytokine-dependent activation of epidermal DCs. MUTZ-LCs revealed high TLR2 expression and responded robustly to TLR2 engagement, confirmed by increased CD83, CD86, PD-L1 and IDO expression, upregulated IL-6, IL-12p40 and IL-23p19 mRNA levels IL-8 release. TLR2 activation reduced CCR6 and elevated CCR7 mRNA expression and induced migration of MUTZ-LCs towards CCL21. Similar results were obtained by stimulation with pro-inflammatory cytokines TNF-α and IL-1ß whereas ligands of TLR3 and TLR4 failed to induce a fully mature phenotype. Despite limited cytokine gene expression and production for TLR2-activated MUTZ-LCs, co-culture with naive CD4(+) T cells led to significantly increased IFN-γ and IL-22 levels indicating Th1 differentiation independent of IL-12. TLR2-mediated effects were blocked by the putative TLR2/1 antagonist CU-CPT22, however, no selectivity for either TLR2/1 or TLR2/6 was observed. Computer-aided docking studies confirmed non-selective binding of the TLR2 antagonist. Taken together, our results indicate a critical role for TLR2 signalling in MUTZ-LCs considering the leukemic origin of the generated Langerhans-like cells.

The impact of molecular dynamics on drug design: applications for the characterization of ligand-macromolecule complexes.

Among all tools available to design new drugs, molecular dynamics (MD) simulations have become an essential technique. Initially developed to investigate molecular models with a limited number of atoms, computers now enable investigations of large macromolecular systems with a simulation time reaching the microsecond range. The reviewed articles cover four years of research to give an overview on the actual impact of MD on the current medicinal chemistry landscape with a particular emphasis on studies of ligand-protein interactions. With a special focus on studies combining computational approaches with data gained from other techniques, this review shows how deeply embedded MD simulations are in drug design strategies and articulates what the future of this technique could be.

Identification of PPARγ Agonists from Natural Sources Using Different In Silico Approaches.

Peroxisome proliferator-activated receptor γ plays an important role in lipid and glucose homeostasis and is the target of many drug discovery investigations because of its role in diseases such as type 2 diabetes. Activation of peroxisome proliferator-activated receptor γ by agonists leads to a conformational change in the ligand-binding domain altering the transcription of several target genes involved in glucose and lipid metabolism, resulting in, for example, facilitation of glucose and lipid uptake and amelioration of insulin resistance, and other effects that are important in the treatment of type 2 diabetes. Peroxisome proliferator-activated receptor γ partial agonists are compounds with diminished agonist efficacy compared to full agonists; however, they maintain the antidiabetic effect of full agonists but do not induce the same magnitude of side effects. This mini-review gives a short introduction to in silico screening methods and recent research advances using computational approaches to identify peroxisome proliferator-activated receptor γ agonists, especially partial agonists, from natural sources and how these ligands bind to the peroxisome proliferator-activated receptor γ in order to better understand their biological effects.

Prospective virtual screening in a sparse data scenario: design of small-molecule TLR2 antagonists.

Toll-like receptors (TLRs) are critical signaling molecules with roles in various severe clinical conditions such as sepsis and rheumatoid arthritis, and have therefore been advocated as promising drug targets for the treatment of these diseases. The aim of this study was to discover small-molecule antagonists of TLR2 by computer-aided drug design. This goal poses several challenges due to the lack of available data on TLR2 modulators. To overcome these hurdles we developed a combined structure- and ligand-based virtual screening approach. First, we calculated molecular interaction fields of the TLR2 binding site to derive a structure-based 3D pharmacophore, which was then used for virtual screening. We then performed a two-step shape- and feature-based similarity search using known TLR2 ligands as query structures. A selection of virtual screening hits was biologically tested in a cell-based assay for TLR2 signaling inhibition, leading to the identification of several compounds with antagonistic activity (IC50 values) in the low-micromolar range.

In silico virtual screening approaches for anti-viral drug discovery.

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.:

Development of immune-specific interaction potentials and their application in the multi-agent-system VaccImm.

Peptide vaccination in cancer therapy is a promising alternative to conventional methods. However, the parameters for this personalized treatment are difficult to access experimentally. In this respect, in silico models can help to narrow down the parameter space or to explain certain phenomena at a systems level. Herein, we develop two empirical interaction potentials specific to B-cell and T-cell receptor complexes and validate their applicability in comparison to a more general potential. The interaction potentials are applied to the model VaccImm which simulates the immune response against solid tumors under peptide vaccination therapy. This multi-agent system is derived from another immune system simulator (C-ImmSim) and now includes a module that enables the amino acid sequence of immune receptors and their ligands to be taken into account. The multi-agent approach is combined with approved methods for prediction of major histocompatibility complex (MHC)-binding peptides and the newly developed interaction potentials. In the analysis, we critically assess the impact of the different modules on the simulation with VaccImm and how they influence each other. In addition, we explore the reasons for failures in inducing an immune response by examining the activation states of the immune cell populations in detail.In summary, the present work introduces immune-specific interaction potentials and their application to the agent-based model VaccImm which simulates peptide vaccination in cancer therapy.

CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge.

During the development of methods for cancer diagnosis and treatment, a vast amount of information is generated. Novel cancer target proteins have been identified and many compounds that activate or inhibit cancer-relevant target genes have been developed. This knowledge is based on an immense number of experimentally validated compound-target interactions in the literature, and excerpts from literature text mining are spread over numerous data sources. Our own analysis shows that the overlap between important existing repositories such as Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), Pharmacogenomics Knowledge Base (PharmGKB) and DrugBank as well as between our own literature mining for cancer-annotated entries is surprisingly small. In order to provide an easy overview of interaction data, it is essential to integrate this information into a single, comprehensive data repository. Here, we present CancerResource, a database that integrates cancer-relevant relationships of compounds and targets from (i) our own literature mining and (ii) external resources complemented with (iii) essential experimental and supporting information on genes and cellular effects. In order to facilitate an overview of existing and supporting information, a series of novel information connections have been established. CancerResource addresses the spectrum of research on compound-target interactions in natural sciences as well as in individualized medicine; CancerResource is available at: http://bioinformatics.charite.de/cancerresource/.

PROMISCUOUS: a database for network-based drug-repositioning.

The procedure of drug approval is time-consuming, costly and risky. Accidental findings regarding multi-specificity of approved drugs led to block-busters in new indication areas. Therefore, the interest in systematically elucidating new areas of application for known drugs is rising. Furthermore, the knowledge, understanding and prediction of so-called off-target effects allow a rational approach to the understanding of side-effects. With PROMISCUOUS we provide an exhaustive set of drugs (25,000), including withdrawn or experimental drugs, annotated with drug-protein and protein-protein relationships (21,500/104,000) compiled from public resources via text and data mining including manual curation. Measures of structural similarity for drugs as well as known side-effects can be easily connected to protein-protein interactions to establish and analyse networks responsible for multi-pharmacology. This network-based approach can provide a starting point for drug-repositioning. PROMISCUOUS is publicly available at http://bioinformatics.charite.de/promiscuous.

Theoretical modeling techniques and their impact on tumor immunology.

Currently, cancer is one of the leading causes of death in industrial nations. While conventional cancer treatment usually results in the patient suffering from severe side effects, immunotherapy is a promising alternative. Nevertheless, some questions remain unanswered with regard to using immunotherapy to treat cancer hindering it from being widely established. To help rectify this deficit in knowledge, experimental data, accumulated from a huge number of different studies, can be integrated into theoretical models of the tumor-immune system interaction. Many complex mechanisms in immunology and oncology cannot be measured in experiments, but can be analyzed by mathematical simulations. Using theoretical modeling techniques, general principles of tumor-immune system interactions can be explored and clinical treatment schedules optimized to lower both tumor burden and side effects. In this paper, we aim to explain the main mathematical and computational modeling techniques used in tumor immunology to experimental researchers and clinicians. In addition, we review relevant published work and provide an overview of its impact to the field.