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

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

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/

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

Modulation der ersten Barriere der Immunantwort: Design von TLR2-Antagonisten und Untersuchung ihrer Bindemodi

Toll-like receptors (TLRs) play a pivotal role in the onset of innate immunity against invading microbial. Recently, they have been identified as potential drug-targets due to their role in the advent of various severe clinical conditions such as cancer, rheumatoid arthritis and pathogen sepsis. The main goal of this work was to discover small molecule TLR2 antagonists by virtual screening and molecular modeling. For this purpose, we developed and performed a combined structure- and ligand-based virtual screening workflow. In the structure-based part of the study, we first identified a putative small molecule binding site of which we then calculated molecular interaction fields (MIFs). These were used to identify key interactions necessary for ligand binding that were later transferred into 3D pharmacophores. Subsequently, the generated models were employed to perform virtual screening of a commercial library comprising about 3 million compounds. Through consecutive docking and visual inspection the generated virtual hits were prioritized and compounds selected for biological validation. This led to one out of ten biologically active TLR2 antagonists. In the ligand-based part of the study, a two-stepped shape- and feature-based search was performed. The first screening was performed using two known TLR2 agonists and one TLR2 signaling inhibitor with unknown target protein as query structures to search the compound collection provided by the NCI. In the second search, the two TLR2 agonists were employed as query structures. Furthermore, one TLR2 signaling inhibitor and the two most promising hits from the first virtual screening were included as query structures. Virtual screening steps were followed by experimental validation of the selected compounds. In total, nine TLR2 antagonists were identified by ligand-based virtual screening. Overall, 75 compounds were selected through virtual screening and biologically tested in an NF-κB reporter assay. In total, ten compounds showed antagonistic activity in this assay and were further characterized with regard to their effect on cytokine production in human monocytes. All compounds showed IC50 values in the micromolar range, three of them in the low micromolar range (<5 µM). In addition, four TLR2 agonists could be identified. In a next step, the binding modes of the discovered TLR2 modulators were elucidated by docking studies. Furthermore, we analyzed a series of TLR2 antagonists that were published during the course of this study. The binding mode of a series of benzotropolone derivatives could consistently be determined by docking. In the last part of our study, we integrated these compounds and the antagonists discovered in our study into a 3D pharmacophore model collection including all currently available information on TLR2 antagonism. The models were optimized and validated and can now be used to discover further TLR2 antagonists through virtual screening.Toll-like Rezeptoren (TLRs) spielen eine entscheidende Rolle bei der Aktivierung der angeborenen Immunantwort gegenüber mikrobiellen Krankheitserregern. Aufgrund ihrer Rolle bei der Enstehung verschiedener Krankheiten wie Krebs, rheumatoider Arthritis und Sepsis sind TLRs kürzlich als Target für die Arzneistoffentwicklung etabliert worden. Dementsprechend war das Hauptziel dieser Arbeit die Entdeckung von TLR2 Antagonisten durch virtuelles Screening und Molecular Modelling. Zu diesem Zweck wurde ein kombiniertes struktur- und liganden-basiertes virtuelles Screening durchgeführt. Im strukturbasierten Teil der Studie wurde als erstes eine putative Bindestelle für kleine Moleküle identifiziert, von der anschließend molecular interaction fields (MIFs) berechnet wurden. Hierdurch konnten für die Bindung von Liganden notwendige Interaktionen erkannt und mittels 3D Pharmakophoren modelliert werden. Im anschließenden Schritt wurde ein virtuelles Screening einer Datenbank mit ca. 3 Millionen kommerziell verfügbaren Verbindungen durchgeführt. Virtuelle Hits wurden durch Protein- Liganden-Docking und visuelle Inspektion priorisiert und zehn Moleküle für die biologische Testung ausgewählt. Dies führte zu einem bisher unbekannten, biologisch aktiven TLR2 Antagonisten. Im ligandenbasierten Teil der Studie wurde ein zweistufiges virtuelles Screening durch sterische Überlagerung durchgeführt. Im ersten Teil wurden zwei bekannte TLR2 Agonisten und ein Inhibitor der von TLR2 induzierten Signalkaskade als Suchstrukturen verwendet. Im zweiten Teil wurden neben den beiden bekannten TLR2 Agonisten ein weiterer Inhibitor der Signalkaskade und die zwei vielversprechendsten Hits aus dem ersten Teil benutzt. Die durch virtuelles Screening ausgewählten Strukturen wurden biologisch validiert. Insgesamt führte der ligandenbasierte Teil der Arbeit zu neun bisher unbekannten, biologisch aktiven TLR2 Antagonisten. Insgesamt wurden 75 Verbindungen durch virtuelles Screening ausgewählt und biologisch in einem NF-κB-Reporter-Assay getestet. Zehn davon zeigten antagonistische Aktivität und wurden daraufhin in Hinblick auf ihre Wirkung auf die Produktion von Zytokinen in humanen Monozyten untersucht und ihre IC50-Werte bestimmt. Alle Verbindungen zeigten Aktivität im mikromolaren Bereich, drei unter 5 µM. Darüber hinaus konnten vier TLR2 Agonisten entdeckt werden. Daraufhin wurde der Bindungsmodus der identifizierten TLR2 Modulatoren durch Dockingstudien aufgeklärt. Desweiteren analysierten wir eine Reihe von TLR2 Antagonisten, die im Verlauf dieser Arbeit anderweitig veröffentlicht wurden. Der Bindungsmodus einer Reihe von Benzotropolon-Derivanten wurde durch molekulares Docking bestimmt. Im letzten Teil unserer Studie wurde eine Sammlung von 3D Pharmakophoren generiert, die die gesamten derzeit verfügbaren Informationen über TLR2 Antagonisten integriert. Die Modelle können nun dazu verwendet werden, durch virtuelles Screening weitere TLR2 Antagonisten zu entdecken

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