Analysis of Nuisance Substructures and Aggregators in a Comprehensive Database of Food Chemical Compounds

Privileged natural product compound classes for anti-inflammatory drug development

Covering: up to early 2025Privileged compound classes of anti-inflammatory natural products are those where there are many reported members that possess anti-inflammatory properties. The identification of these classes is of particular relevance to drug discovery, as they could serve as valuable starting points in developing effective and safe anti-inflammatory agents. The privileged compound classes of natural products include the polyphenols, coumarins, labdane diterpenoids, sesquiterpene lactones, isoquinoline and indole alkaloids, each offering a variety of molecular scaffolds and functional groups that enable diverse interactions with biological targets. From a medicinal chemistry point of view, natural products are both a boon and a bane. The multi-targeting nature of natural products is a boon in the treatment of multi-factorial diseases such as inflammation, but promiscuity, poor potency and pharmacokinetic properties are significant hurdles that must be addressed to ensure these compounds can be effectively used as therapeutics. In addition, there are continued controversies regarding the efficacies of some of these natural products that will continue to polarise their use. In this review, examples of natural products of six privileged compound classes will be discussed for their potential use and possible further development as anti-inflammatory drugs.

Identifying and Filling the Chemobiological Gaps of Gut Microbial Metabolites

Human gut microbial metabolites are currently undergoing much research due to their involvement in multiple biological processes that are important for health, including immunity, metabolism, nutrition, and the nervous system. Metabolites exert their effect through interaction with host and bacterial proteins, suggesting the use of "metabolite-mimetic" molecules as drugs and nutraceutics. In the present work, we retrieve and analyze the full set of published interactions of these compounds with human and microbiome-relevant proteins and find patterns in their structure, chemical class, target class, and biological origins. In addition, we use virtual screening to expand (more than 4-fold) the interactions, validate them with retrospective analyses, and use bioinformatic tools to prioritize them based on biological relevance. In this way, we fill many of the chemobiological gaps observed in the published data. By providing these interactions, we expect to speed up the full clarification of the chemobiological space of these compounds by suggesting many reliable predictions for fast, focused experimental testing.

Chemical Multiverse and Diversity of Food Chemicals

Food chemicals have a fundamental role in our lives, with an extended impact on nutrition, disease prevention, and marked economic implications in the food industry. The number of food chemical compounds in public databases has substantially increased in the past few years, which can be characterized using chemoinformatics approaches. We and other groups explored public food chemical libraries containing up to 26,500 compounds. This study aimed to analyze the chemical contents, diversity, and coverage in the chemical space of food chemicals and additives and, from here on, food components. The approach to food components addressed in this study is a public database with more than 70,000 compounds, including those predicted via omics techniques. It was concluded that food components have distinctive physicochemical properties and constitutional descriptors despite sharing many chemical structures with natural products. Food components, on average, have large molecular weights and several apolar structures with saturated hydrocarbons. Compared to reference databases, food component structures have low scaffold and fingerprint-based diversity and high structural complexity, as measured by the fraction of sp3 carbons. These structural features are associated with a large fraction of macronutrients as lipids. Lipids in food components were decompiled by an analysis of the maximum common substructures. The chemical multiverse representation of food chemicals showed a larger coverage of chemical space than natural products and FDA-approved drugs by using different sets of representations.

The Synthesis and Antibacterial Properties of Pillar[5]arene with Streptocide Fragments

The growing problem of bacterial resistance to antimicrobials actualizes the development of new approaches to solve this challenge. Supramolecular chemistry tools can overcome the limited bacterial resistance and side effects of classical sulfonamides that hinder their use in therapy. Here, we synthesized a number of pillar[5]arenes functionalized with different substituents, determined their ability to self-association using DLS, and characterized antimicrobial properties against S. typhimurium, K. pneumoniae, P. aeruginosa, S. epidermidis, S. aureus via a resazurin test. Biofilm prevention concentration was calculated for an agent with established antimicrobial activity by the crystal-violet staining method. We evaluated the mutagenicity of the macrocycle using the Ames test and its ability to affect the viability of A549 and LEK cells in the MTT-test. It was shown that macrocycle functionalized with sulfonamide residues exhibited antimicrobial activity an order higher than pure streptocide and also revealed the ability to prevent biofilm formation of S. aureus and P. aeruginosa. The compound did not show mutagenic activity and exhibited low toxicity to eukaryotic cells. The obtained results allow considering modification of the macrocyclic platforms with classic antimicrobials as an opportunity to give them a "second life" and return to practice with improved properties.

Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs

Gut-targeted drugs provide a new drug modality besides that of oral, systemic molecules, that could tap into the growing knowledge of gut metabolites of bacterial or host origin and their involvement in biological processes and health through their interaction with gut targets (bacterial or host, too). Understanding the properties of gut metabolites can provide guidance for the design of gut-targeted drugs. In the present work we analyze a large set of gut metabolites, both shared with serum or present only in gut, and compare them with oral systemic drugs. We find patterns specific for these two subsets of metabolites that could be used to design drugs targeting the gut. In addition, we develop and openly share a Super Learner model to predict gut permanence, in order to aid in the design of molecules with appropriate profiles to remain in the gut, resulting in molecules with putatively reduced secondary effects and better pharmacokinetics.

Systematic Analysis and Prediction of the Target Space of Bioactive Food Compounds: Filling the Chemobiological Gaps

Food compounds and their molecular interactions are crucial for health and provide new chemotypes and targets for drug and nutraceutic design. Here, we retrieve and analyze the complete set of published interactions of food compounds with human proteins using the FooDB as a compound set and ChEMBL as a source of interactions. The data are analyzed in terms of 19 target classes and 19 compound classes, showing a small fraction of target assignment for the compounds (1.6%) and unraveling multiple gaps in the chemobiological space for these molecules. By using well-established cheminformatic approaches [similarity ensemble approach (SEA) combined with the maximum Tanimoto coefficient to the nearest bioactive, "SEA + TC"], we achieve a much enhanced target assignment (64.2%), filling many of the gaps with target hypothesis for fast focused testing. By publishing these data sets and analyses, we expect to provide a set of resources to speed up the full clarification of the chemobiological space of food compounds, opening new opportunities for drug and nutraceutic design.

A Review on the Foodomics Based on Liquid Chromatography Mass Spectrometry

Due to the globalization of food production and distribution, the food chain has become increasingly complex, making it more difficult to evaluate unexpected food changes. Therefore, establishing sensitive, robust, and cost-effective analytical platforms to efficiently extract and analyze the food-chemicals in complex food matrices is essential, however, challenging. LC/MS-based metabolomics is the key to obtain a broad overview of human metabolism and understand novel food science. Various metabolomics approaches (e.g., targeted and/or untargeted) and sample preparation techniques in food analysis have their own advantages and limitations. Selecting an analytical platform that matches the characteristics of the analytes is important for food analysis. This review highlighted the recent trends and applications of metabolomics based on "foodomics" by LC-MS and provides the perspectives and insights into the methodology and various sample preparation techniques in food analysis.

Updated Prediction of Aggregators and Assay-Interfering Substructures in Food Compounds

Positive outcomes in biochemical and biological assays of food compounds may appear due to the well-described capacity of some compounds to form colloidal aggregates that adsorb proteins, resulting in their denaturation and loss of function. This phenomenon can lead to wrongly ascribing mechanisms of biological action for these compounds (false positives) as the effect is nonspecific and promiscuous. Similar false positives can show up due to chemical (photo)reactivity, redox cycling, metal chelation, interferences with the assay technology, membrane disruption, etc., which are more frequently observed when the tested molecule has some definite interfering substructures. Although discarding false positives can be achieved experimentally, it would be very useful to have in advance a prognostic value for possible aggregation and/or interference based only in the chemical structure of the compound tested in order to be aware of possible issues, help in prioritization of compounds to test, design of appropriate assays, etc. Previously, we applied cheminformatic tools derived from the drug discovery field to identify putative aggregators and interfering substructures in a database of food compounds, the FooDB, comprising 26,457 molecules at that time. Here, we provide an updated account of that analysis based on a current, much-expanded version of the FooDB, comprising a total of 70,855 compounds. In addition, we also apply a novel machine learning model (SCAM Detective) to predict aggregators with 46-53% increased accuracies over previous models. In this way, we expect to provide the researchers in the mode of action of food compounds with a much improved, robust, and widened set of putative aggregators and interfering substructures of food compounds.