A microbially produced AhR ligand promotes a Tph1-driven tolerogenic program in multiple sclerosis.

Multiple sclerosis is a debilitating autoimmune disease, characterized by chronic inflammation of the central nervous system. While the significance of the gut microbiome on multiple sclerosis pathogenesis is established, the underlining mechanisms are unknown. We found that serum levels of the microbial postbiotic tryptophan metabolite indole-3-carboxaldehyde (3-IAld) inversely correlated with disease duration in multiple sclerosis patients. Much like the host-derived tryptophan derivative L-Kynurenine, 3-IAld would bind and activate the Aryl hydrocarbon Receptor (AhR), which, in turn, controls endogenous tryptophan catabolic pathways. As a result, in peripheral lymph nodes, microbial 3-IAld, affected mast-cell tryptophan metabolism, forcing mast cells to produce serotonin via Tph1. We thus propose a protective role for AhR-mast-cell activation driven by the microbiome, whereby natural metabolites or postbiotics will have a physiological role in immune homeostasis and may act as therapeutic targets in autoimmune diseases.

Exploiting Vector Pattern Diversity of Molecular Scaffolds for Cheminformatics Tasks in Drug Discovery.

Chemical diversity is challenging to describe objectively. Despite this, various notions of chemical diversity are used throughout the medicinal chemistry optimization process in drug discovery. In this work, we show the usefulness of considering exploited vectors during different phases of the drug design process to provide a quantitative and objective description of chemical diversity. We have developed a concise and fast approach to enumerate and analyze the exploited vector patterns (EVPs) of molecular compound series, which can then be used in archetypal compound selection tasks, from hit matter identification to hit expansion and lead optimization. We first show that EVPs can be used to assess the progressibility of compounds in a fragment library design exercise. By considering EVPs, we then show how a set of compounds can be prioritized for hit expansion using EVP-based, customizable diversity sampling approaches, reducing the time taken and mitigating human biases. We also show that EVPs are a useful tool to analyze SAR data, offering the chance to uncover correlations between different vectors without predetermining the molecular scaffold structures. The codes used to perform these tasks are presented as easy-to-use Jupyter notebooks, which can be readily adapted for further related tasks.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (https://cansar.ai) is the largest public cancer drug discovery and translational research knowledgebase. Now hosted in its new home at MD Anderson Cancer Center, canSAR integrates billions of experimental measurements from across molecular profiling, pharmacology, chemistry, structural and systems biology. Moreover, canSAR applies a unique suite of machine learning algorithms designed to inform drug discovery. Here, we describe the latest updates to the knowledgebase, including a focus on significant novel data. These include canSAR's ligandability assessment of AlphaFold; mapping of fragment-based screening data; and new chemical bioactivity data for novel targets. We also describe enhancements to the data and interface.

canSAR chemistry registration and standardization pipeline.

BACKGROUND: Integration of medicinal chemistry data from numerous public resources is an increasingly important part of academic drug discovery and translational research because it can bring a wealth of important knowledge related to compounds in one place. However, different data sources can report the same or related compounds in various forms (e.g., tautomers, racemates, etc.), thus highlighting the need of organising related compounds in hierarchies that alert the user on important bioactivity data that may be relevant. To generate these compound hierarchies, we have developed and implemented canSARchem, a new compound registration and standardization pipeline as part of the canSAR public knowledgebase. canSARchem builds on previously developed ChEMBL and PubChem pipelines and is developed using KNIME. We describe the pipeline which we make publicly available, and we provide examples on the strengths and limitations of the use of hierarchies for bioactivity data exploration. Finally, we identify canonicalization enrichment in FDA-approved drugs, illustrating the benefits of our approach. RESULTS: We created a chemical registration and standardization pipeline in KNIME and made it freely available to the research community. The pipeline consists of five steps to register the compounds and create the compounds' hierarchy: 1. Structure checker, 2. Standardization, 3. Generation of canonical tautomers and representative structures, 4. Salt strip, and 5. Generation of abstract structure to generate the compound hierarchy. Unlike ChEMBL's RDKit pipeline, we carry out compound canonicalization ahead of getting the parent structure, similar to PubChem's OpenEye pipeline. canSARchem has a lower rejection rate compared to both PubChem and ChEMBL. We use our pipeline to assess the impact of grouping the compounds in hierarchies for bioactivity data exploration. We find that FDA-approved drugs show statistically significant sensitivity to canonicalization compared to the majority of bioactive compounds which demonstrates the importance of this step. CONCLUSIONS: We use canSARchem to standardize all the compounds uploaded in canSAR (> 3 million) enabling efficient data integration and the rapid identification of alternative compound forms with useful bioactivity data. Comparison with PubChem and ChEMBL pipelines evidenced comparable performances in compound standardization, but only PubChem and canSAR canonicalize tautomers and canSAR has a slightly lower rejection rate. Our results highlight the importance of compound hierarchies for bioactivity data exploration. We make canSARchem available under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0) at https://gitlab.icr.ac.uk/cansar-public/compound-registration-pipeline .

Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.

BACKGROUND: Drug-induced liver injury (DILI) is a major safety concern characterized by a complex and diverse pathogenesis. In order to identify DILI early in drug development, a better understanding of the injury and models with better predictivity are urgently needed. One approach in this regard are in silico models which aim at predicting the risk of DILI based on the compound structure. However, these models do not yet show sufficient predictive performance or interpretability to be useful for decision making by themselves, the former partially stemming from the underlying problem of labeling the in vivo DILI risk of compounds in a meaningful way for generating machine learning models. RESULTS: As part of the Critical Assessment of Massive Data Analysis (CAMDA) "CMap Drug Safety Challenge" 2019 ( http://camda2019.bioinf.jku.at ), chemical structure-based models were generated using the binarized DILIrank annotations. Support Vector Machine (SVM) and Random Forest (RF) classifiers showed comparable performance to previously published models with a mean balanced accuracy over models generated using 5-fold LOCO-CV inside a 10-fold training scheme of 0.759 ± 0.027 when predicting an external test set. In the models which used predicted protein targets as compound descriptors, we identified the most information-rich proteins which agreed with the mechanisms of action and toxicity of nonsteroidal anti-inflammatory drugs (NSAIDs), one of the most important drug classes causing DILI, stress response via TP53 and biotransformation. In addition, we identified multiple proteins involved in xenobiotic metabolism which could be novel DILI-related off-targets, such as CLK1 and DYRK2. Moreover, we derived potential structural alerts for DILI with high precision, including furan and hydrazine derivatives; however, all derived alerts were present in approved drugs and were over specific indicating the need to consider quantitative variables such as dose. CONCLUSION: Using chemical structure-based descriptors such as structural fingerprints and predicted protein targets, DILI prediction models were built with a predictive performance comparable to previous literature. In addition, we derived insights on proteins and pathways statistically (and potentially causally) linked to DILI from these models and inferred new structural alerts related to this adverse endpoint.

canSAR: update to the cancer translational research and drug discovery knowledgebase.

canSAR (http://cansar.icr.ac.uk) is the largest, public, freely available, integrative translational research and drug discovery knowledgebase for oncology. canSAR integrates vast multidisciplinary data from across genomic, protein, pharmacological, drug and chemical data with structural biology, protein networks and more. It also provides unique data, curation and annotation and crucially, AI-informed target assessment for drug discovery. canSAR is widely used internationally by academia and industry. Here we describe significant developments and enhancements to the data, web interface and infrastructure of canSAR in the form of the new implementation of the system: canSARblack. We demonstrate new functionality in aiding translation hypothesis generation and experimental design, and show how canSAR can be adapted and utilised outside oncology.

The Stone Guest: How Does pH Affect Binding Properties of PD-1/PD-L1 Inhibitors?

The interaction between programmed cell death-1 (PD-1) and its ligand PD-L1 activates a coinhibitory signal that blocks T-cell activation, promoting the immune escape process in the tumor microenvironment. Development of monoclonal antibodies targeting and inhibiting PD-1/PD-L1 interaction as anticancer immunotherapies has proved successful in multiple clinical settings and for various types of cancer. Notwithstanding, limitations exist with the use of these biologics, including drug resistance and narrow therapeutic response rate in a majority of patients, that demand for the design of more efficacious small molecule-based immunotherapies. Alteration of pH in the tumor microenvironment is a key factor that is involved in promoting drug resistance, tumor survival and progression. In this study, we have investigated the effect of pH shifts on binding properties of distinct classes of PD-L1 inhibitors, including macrocyclic peptide and small molecules. Results expand structure-activity relationships of PD-L1 inhibitors, providing insights into structural features and physicochemical properties that are useful for the design of ligands that may escape a drug resistance mechanism associated to variable pH conditions of tumor microenvironment.

Targeting Aryl hydrocarbon receptor for next-generation immunotherapies: Selective modulators (SAhRMs) versus rapidly metabolized ligands (RMAhRLs).

Aryl Hydrocarbon Receptor (AhR) constitutes a major network hub of genomic and non-genomic signaling pathways, connecting host's immune cells to environmental factors. It shapes innate and adaptive immune processes to environmental stimuli with species-, cell- and tissue-type dependent specificity. Although an ever increasing number of studies has thrust AhR into the limelight as attractive target for the development of next-generation immunotherapies, concerns exist on potential safety issues associated with small molecule modulation of the receptor. Selective AhR modulators (SAhRMs) and rapidly metabolized AhR ligands (RMAhRLs) are two classes of receptor agonists that are emerging as interesting lead compounds to bypass AhR-related toxicity in favor of therapeutic effects. In this article, we discuss SAhRMs and RMAhRLs reported in literature, covering concepts underlying their definitions, specific binding modes, structure-activity relationships and AhR-mediated functions.

Tracking Hidden Binding Pockets Along the Molecular Recognition Path of l-Trp to Indoleamine 2,3-Dioxygenase 1.

Indoleamine 2,3-dioxygenase 1 (IDO1) catalyzes the oxidative cleavage of l-Tryptophan (l-Trp) to yield N-formyl-kynurenine in the first and rate limiting step of the kynurenine pathway. Bioactive metabolites, involved in the regulation of important immunological responses and neurological processes, are then produced by downstream enzymes along the pathway. Inhibitors of IDO1 are being designed and developed as therapeutic agents for immuno-oncology. In this work, we investigated the molecular recognition path of l-Trp to IDO1, integrating biophysical methods with supervised molecular dynamics (suMD) and mutagenesis experiments. Results allowed disclosing for the first time high and low dissociation constants of l-Trp to IDO1, and the presence of a metastable interaction site located at the upper part of a channel whose borders are defined by the EF-loop and the C-terminal part of the JK-loop. Collectively, our results provide new clues for the design of next-generation IDO1 ligands.

Binding Mode and Structure-Activity Relationships of ITE as an Aryl Hydrocarbon Receptor (AhR) Agonist.

Discovered as a modulator of the toxic response to environmental pollutants, aryl hydrocarbon receptor (AhR) has recently gained attention for its involvement in various physiological and pathological pathways. AhR is a ligand-dependent transcription factor activated by a large array of chemical compounds, which include metabolites of l-tryptophan (l-Trp) catabolism as endogenous ligands of the receptor. Among these, 2-(1'H-indole-3'-carbonyl)thiazole-4-carboxylic acid methyl ester (ITE) has attracted interest in the scientific community, being endowed with nontoxic, immunomodulatory, and anticancer AhR-mediated functions. So far, no information about the binding mode and interactions of ITE with AhR is available. In this study, we used docking and molecular dynamics to propose a putative binding mode of ITE into the ligand binding pocket of AhR. Mutagenesis studies were then instrumental in validating the proposed binding mode, identifying His 285 and Tyr 316 as important key residues for ligand-dependent receptor activation. Finally, a set of ITE analogues was synthesized and tested to further probe molecular interactions of ITE to AhR and characterize the relevance of specific functional groups in the chemical structure for receptor activity.

Binding properties of different categories of IDO1 inhibitors: a microscale thermophoresis study.

AIM: Inhibition of IDO1 is a strategy pursued in the immune-oncology pipeline for the development of novel anticancer therapies. At odds with an ever-increasing number of inhibitors being disclosed in the literature and patent applications, only very few compounds have hitherto advanced in clinical settings. MATERIALS & METHODS: We have used MicroScale Thermophoresis analysis and docking calculations to assess on a quantitative basis the binding properties of distinct categories of inhibitors to IDO1. RESULTS: Results shed further light on hidden molecular aspects governing the recognition by the enzyme of compounds with different mechanism of inhibition. CONCLUSION: Results pinpoint specific binding features of distinct inhibitors to IDO1 that offer clues for the design of next-generation inhibitors of the enzyme.

Advances in indoleamine 2,3-dioxygenase 1 medicinal chemistry.

Indoleamine 2,3-dioxygenase 1 (IDO1) mediates multiple immunoregulatory processes including the induction of regulatory T cell differentiation and activation, suppression of T cell immune responses and inhibition of dendritic cell function, which impair immune recognition of cancer cells and promote tumor growth. On this basis, this enzyme is widely recognized as a valuable drug target for the development of immunotherapeutic small molecules in oncology. Although medicinal chemistry has made a substantial contribution to the discovery of numerous chemical classes of potent IDO1 inhibitors in the past 20 years, only very few compounds have progressed in clinical trials. In this review, we provide an overview of the current understanding of structure-function relationships of the enzyme, and discuss structure-activity relationships of selected classes of inhibitors that have shaped the hitherto few successes of IDO1 medicinal chemistry. An outlook opinion is also given on trends in the design of next generation inhibitors of the enzyme.

Docking Studies and Molecular Dynamic Simulations Reveal Different Features of IDO1 Structure.

In the last decade, indoleamine 2,3-dioxygenase 1 (IDO1) has attracted a great deal of attention being recognized as key regulator of immunosuppressive pathways in the tumor immuno-editing process. Several classes of inhibitors have been developed as potential anticancer agents, but only few of them have advanced in clinical trials. Hence, the quest of novel potent and selective inhibitors of the enzyme is still active and mostly pursued by structure-based drug design strategies based on early and more recent crystal structures of IDO1. Combining docking studies and molecular dynamic simulations, in this work we have comparatively investigated the structural features of each crystal structure of IDO1. The results pinpoint different features in specific crystal structures of the enzyme that may benefit the medicinal chemistry arena aiding the design of novel potent and selective inhibitors of IDO1.

Ligand binding and functional selectivity of L-tryptophan metabolites at the mouse aryl hydrocarbon receptor (mAhR).

The aryl hydrocarbon receptor (AhR) is a nuclear receptor regulating a wide range of biological and toxicological effects. Metabolites of L-tryptophan are able to bind and activate AhR, providing a link between tryptophan catabolism and a novel mechanism of protective tolerance, referred to as "disease tolerance". The notion that pharmacologic modulation of genes associated with endotoxin tolerance would be beneficial in clinical settings dominated by acute hyperinflammatory responses to infection thrusts AhR into the limelight as an interesting druggable target. Combining homology modeling, docking studies, and molecular dynamic simulations with mutagenesis experiments and gene profiling, in this work we report that 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and two different L-tryptophan metabolites, namely L-Kynurenine and FICZ (6-formylindolo[3,2-b]carbazole), are able to bind to mAhR, exploiting different key interactions with distinct set of fingerprint residues. As a result, they stabilize different conformations of mAhR that, in turn, selectively regulate downstream signaling and transcription of specific target genes. Collectively, these results open new avenues for the design and development of selective AhR modulators that, by targeting specific receptor conformations associated with specific AhR functions, may offer novel therapeutic opportunities in infectious diseases and other morbidity that may be associated with the receptor.