Systematic mining of generally recognized as safe (GRAS) flavor chemicals for bioactive compounds

Artificial intelligence-driven prediction of multiple drug interactions

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

Raspberry consumption: identification of distinct immune-metabolic response profiles by whole blood transcriptome profiling

Numerous studies have reported that diets rich in phenolic compounds are beneficial to immune-metabolic health, yet these effects are heterogeneous and the underlying mechanisms are poorly understood. To investigate the inter-individual variability of the immune-metabolic response to raspberry consumption, whole-blood RNAseq data from 24 participants receiving 280g/day of raspberries for 8 weeks were used for the identification of responsiveness subgroups by using partial least squares-discriminant analysis (PLSDA) and hierarchical clustering. Transcriptomic-based clustering regrouped participants into two distinct subgroups of 13 and 11 participants, so-called responders and non-responders, respectively. Following raspberry consumption, a significant decrease in triglycerides, cholesterol and C-reactive protein levels were found in responders, as compared to non-responders. Two major gene expression components of 100 and 220 genes were identified by sparse PLSDA as those better discriminating responders from non-responders, and functional analysis identified pathways related to cytokine production, leukocyte activation and immune response as significantly enriched with most discriminant genes. As compared to non-responders, the plasma lipidomic profile of responders was characterized by a significant decrease in triglycerides and an increase in phosphatidylcholines following raspberry consumption. Prior to the intervention, a distinct metagenomic profile was identified by PLSDA between responsiveness subgroups, and the Firmicutes-to-Bacteroidota ratio was found significantly lower in responders, as compared to non-responders. Findings point to this transcriptomic-based clustering approach as a suitable tool to identify distinct responsiveness subgroups to raspberry consumption. This approach represents a promising framework to tackle the issue of inter-individual variability in the understanding of the impact of foods on immune-metabolic health.

Machine Learning Uncovers Food- and Excipient-Drug Interactions

Inactive ingredients and generally recognized as safe compounds are regarded by the US Food and Drug Administration (FDA) as benign for human consumption within specified dose ranges, but a growing body of research has revealed that many inactive ingredients might have unknown biological effects at these concentrations and might alter treatment outcomes. To speed up such discoveries, we apply state-of-the-art machine learning to delineate currently unknown biological effects of inactive ingredients—focusing on P-glycoprotein (P-gp) and uridine diphosphate-glucuronosyltransferase-2B7 (UGT2B7), two proteins that impact the pharmacokinetics of approximately 20% of FDA-approved drugs. Our platform identifies vitamin A palmitate and abietic acid as inhibitors of P-gp and UGT2B7, respectively; in silico, in vitro, ex vivo, and in vivo validations support these interactions. Our predictive framework can elucidate biological effects of commonly consumed chemical matter with implications on food-and excipient-drug interactions and functional drug formulation development.

Reker et al. use machine learning to identify biological activities of food and drug additives. Validation confirms vitamin A palmitate as an inhibitor of P-glycoprotein transport and abietic acid as an inhibitor of UGT2b7 metabolism. Such associations have important implications as food-or excipient-drug interactions.

Paradoxical Effects of a Cytokine and an Anticonvulsant Strengthen the Epigenetic/Enzymatic Avenue for Autism Research

Maternal exposure to the valproate short-chain fatty acid (SCFA) during pregnancy is known to possibly induce autism spectrum disorders (ASDs) in the offspring. By contrast, case studies have evidenced positive outcomes of this anticonvulsant drug in children with severe autism. Interestingly, the same paradoxical pattern applies to the IL-17a inflammatory cytokine involved in the immune system regulation. Such joint apparent contradictions can be overcome by pointing out that, among their respective signaling pathways, valproate and IL-17a share an enhancement of the “type A monoamine oxidase” (MAOA) enzyme carried by the X chromosome. In the Guided Propagation (GP) model of autism, such enzymatic rise triggers a prenatal epigenetic downregulation, which, without possible X-inactivation, and when coinciding with genetic expression variants of other brain enzymes, results in the delayed onset of autistic symptoms. The underlying imbalance of synaptic monoamines, serotonin in the first place, would reflect a mismatch between the environment to which the brain metabolism was prepared during gestation and the postnatal actual surroundings. Following a prenatal exposure to molecules that significantly elicit the MAOA gene expression, a daily treatment with the same metabolic impact would tend to recreate the fetal environment and contribute to rebalance monoamines, thus allowing proper neural circuits to gradually develop, provided behavioral re-education. Given the multifaceted other players than MAOA that are involved in the regulation of serotonin levels, potential compensatory effects are surveyed, which may underlie the autism heterogeneity. This explanatory framework opens up prospects regarding autism prevention and treatment, strikingly in line with current advances along the gut microbiome–brain axis.

An enumeration of natural products from microbial, marine and terrestrial sources

The discovery of a new drug is a multidisciplinary and very costly task. One of the major steps is the identification of a lead compound, i.e. a compound with a certain degree of potency and that can be chemically modified to improve its activity, metabolic properties, and pharmacokinetics profiles. Terrestrial sources (plants and fungi), microbes and marine organisms are abundant resources for the discovery of new structurally diverse and biologically active compounds. In this chapter, an attempt has been made to quantify the numbers of known published chemical structures (available in chemical databases) from natural sources. Emphasis has been laid on the number of unique compounds, the most abundant compound classes and the distribution of compounds in terrestrial and marine habitats. It was observed, from the recent investigations, that ~500,000 known natural products (NPs) exist in the literature. About 70 % of all NPs come from plants, terpenoids being the most represented compound class (except in bacteria, where amino acids, peptides, and polyketides are the most abundant compound classes). About 2,000 NPs have been co-crystallized in PDB structures.

Acute ingestion of rosemary water: Evidence of cognitive and cerebrovascular effects in healthy adults

BACKGROUND

The use of herbal extracts and supplements to enhance health and wellbeing is increasing in western society.

AIMS

This study investigated the impact of the acute ingestion of a commercially available water containing an extract and hydrolat of rosemary ( Rosmarinus officinalis L. syn. Salvia rosmarinus Schleid.). Aspects of cognitive functioning, mood and cerebrovascular response measured by near-infrared spectroscopy provided the dependent variables.

METHODS

Eighty healthy adults were randomly allocated to consume either 250 mL of rosemary water or plain mineral water. They then completed a series of computerised cognitive tasks, followed by subjective measures of alertness and fatigue. Near-infrared spectroscopy monitored levels of total, oxygenated and deoxygenated haemoglobin at baseline and throughout the cognitive testing procedure.

RESULTS

Analysis of the data revealed a number of statistically significant, small, beneficial effects of rosemary water on cognition, consistent with those found previously for the inhalation of the aroma of rosemary essential oil. Of particular interest here are the cerebrovascular effects noted for deoxygenated haemoglobin levels during cognitive task performance that were significantly higher in the rosemary water condition. This represents a novel finding in this area, and may indicate a facilitation of oxygen extraction at times of cognitive demand.

CONCLUSION

Taken together the data suggest potential beneficial properties of acute consumption of rosemary water. The findings are discussed in terms of putative metabolic and cholinergic mechanisms.

Analysis of a large food chemical database: chemical space, diversity, and complexity

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FoodDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of "Food Informatics". Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections.

Analysis of a large food chemical database: chemical space, diversity, and complexity

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FooDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of "Food Informatics". Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections. An additional future direction of this work is to use the list of 3,228 polyphenolic compounds identified in this work to enhance the on-going polyphenol-protein interactome studies.

Correcting false positive medium-chain acyl-CoA dehydrogenase deficiency results from newborn screening; synthesis, purification, and standardization of branched-chain C8 acylcarnitines for use in their selective and accurate absolute quantitation by UHPLC-MS/MS

While selectively quantifying acylcarnitines in thousands of patient samples using UHPLC-MS/MS, we have occasionally observed unidentified branched-chain C8 acylcarnitines. Such observations are not possible using tandem MS methods, which generate pseudo-quantitative acylcarnitine "profiles". Since these "profiles" select for mass alone, they cannot distinguish authentic signal from isobaric and isomeric interferences. For example, some of the samples containing branched-chain C8 acylcarnitines were, in fact, expanded newborn screening false positive "profiles" for medium-chain acyl-CoA dehydrogenase deficiency (MCADD). Using our fast, highly selective, and quantitatively accurate UHPLC-MS/MS acylcarnitine determination method, we corrected the false positive tandem MS results and reported the sample results as normal for octanoylcarnitine (the marker for MCADD). From instances such as these, we decided to further investigate the presence of branched-chain C8 acylcarnitines in patient samples. To accomplish this, we synthesized and chromatographically characterized several branched-chain C8 acylcarnitines (in addition to valproylcarnitine): 2-methylheptanoylcarnitine, 6-methylheptanoylcarnitine, 2,2-dimethylhexanoylcarnitine, 3,3-dimethylhexanoylcarnitine, 3,5-dimethylhexanoylcarnitine, 2-ethylhexanoylcarnitine, and 2,4,4-trimethylpentanoylcarnitine. We then compared their behavior with branched-chain C8 acylcarnitines observed in patient samples and demonstrated our ability to chromographically resolve, and thus distinguish, octanoylcarnitine from branched-chain C8 acylcarnitines, correcting false positive MCADD results from expanded newborn screening.

Internet Databases of the Properties, Enzymatic Reactions, and Metabolism of Small Molecules-Search Options and Applications in Food Science

Internet databases of small molecules, their enzymatic reactions, and metabolism have emerged as useful tools in food science. Database searching is also introduced as part of chemistry or enzymology courses for food technology students. Such resources support the search for information about single compounds and facilitate the introduction of secondary analyses of large datasets. Information can be retrieved from databases by searching for the compound name or structure, annotating with the help of chemical codes or drawn using molecule editing software. Data mining options may be enhanced by navigating through a network of links and cross-links between databases. Exemplary databases reviewed in this article belong to two classes: tools concerning small molecules (including general and specialized databases annotating food components) and tools annotating enzymes and metabolism. Some problems associated with database application are also discussed. Data summarized in computer databases may be used for calculation of daily intake of bioactive compounds, prediction of metabolism of food components, and their biological activity as well as for prediction of interactions between food component and drugs.

A chemical space odyssey of inhibitors of histone deacetylases and bromodomains

The interest in epigenetic drug and probe discovery is growing as reflected in the large amount of structure-epigenetic activity information available.

Discovery and development of DNA methyltransferase inhibitors using in silico approaches

Multiple strategies have evolved during the past few years to advance epigenetic compounds targeting DNA methyltransferases (DNMTs). Significant progress has been made in HTS, lead optimization and determination of 3D structures of DNMTs. In light of the emerging concept of epi-informatics, computational approaches are employed to accelerate the development of DNMT inhibitors helping to screen chemical databases, mine the DNMT-relevant chemical space, uncover SAR and design focused libraries. Computational methods also synergize with natural-product-based drug discovery and drug repurposing. Herein, we survey the latest developments of in silico approaches to advance epigenetic drug and probe discovery targeting DNMTs.

Epigenetic molecular recognition: a biomolecular modeling perspective

The abnormal regulation of epigenetic protein families is associated with the onset and progression of various human diseases. However, epigenetic processes remain relatively obscure at the molecular level, thus preventing the rational design of chemical therapeutics. An array of robust computational and modeling approaches can complement experiments to shed light on the complex mechanisms of epigenetic molecular recognition and can guide medicinal chemists in designing selective and potent drug molecules. Herein we present a review of studies focused on epigenetic molecular recognition from a biomolecular modeling viewpoint. Although the known epigenetic targets are numerous, this review focuses on the more limited protein families on which computational modeling has been successfully applied. Therefore, we review three main topics: 1) histone deacetylases, 2) histone demethylases, and 3) histone tail dynamics. A brief review of the biological background and biomedical relevance is presented for each topic, followed by a detailed discussion of the computational studies and their relevance.