Terry L. Peppard

Chemoinformatic analysis of GRAS (Generally Recognized as Safe) flavor chemicals and natural products.

Food materials designated as “Generally Recognized as Safe” (GRAS) are attracting the attention of researchers in their attempts to systematically identify compounds with putative health-related benefits. In particular, there is currently a great deal of interest in exploring possible secondary benefits of flavor ingredients, such as those relating to health and wellness. One step in this direction is the comprehensive characterization of the chemical structures contained in databases of flavoring substances. Herein, we report a comprehensive analysis of the recently updated FEMA GRAS list of flavoring substances (discrete chemical entities only). Databases of natural products, approved drugs and a large set of commercial molecules were used as references. Remarkably, natural products continue to be an important source of bioactive compounds for drug discovery and nutraceutical purposes. The comparison of five collections of compounds of interest was performed using molecular properties, rings, atom counts and structural fingerprints. It was found that the molecular size of the GRAS flavoring substances is, in general, smaller cf. members of the other databases analyzed. The lipophilicity profile of the GRAS database, a key property to predict human bioavailability, is similar to approved drugs. Several GRAS chemicals overlap to a broad region of the property space occupied by drugs. The GRAS list analyzed in this work has high structural diversity, comparable to approved drugs, natural products and libraries of screening compounds. This study represents one step towards the use of the distinctive features of the flavoring chemicals contained in the GRAS list and natural products to systematically search for compounds with potential health-related benefits.

Characterization of a comprehensive flavor database

Flavor perception involves, among a number of physiological and psychological processes, the recognition of chemicals by olfactory and taste receptors. The highly complex and multidimensional nature of flavor perception challenges our ability to both predict and design new flavor entities. Toward this endeavor, classifications of flavor descriptors have been proposed. Here, we developed a fingerprint‐based representation of a large data set comprising 4181 molecules taken from the commercially available Leffingwell & Associates Canton, Georgia, USA database marketed as Flavor‐Base Pro© 2010. Flavor descriptions of the materials in this database were composite descriptions, collected from numerous sources over the course of more than 40 years. The flavor descriptors were referenced against a detailed and authoritative sensory lexicon (ASTM, American Society for Testing and Materials publication DS 66) comprising 662 flavor attributes. Comparison of clustering analysis, principal component analysis, and descriptor associations provided similar conclusions for various mutually correlated descriptors. Regarding analysis of the flavor similarity of the molecules, the clustering performed provided a means for the quick selection of molecules with either high or low flavor similarity description. Preliminary comparison of the chemical structures to the flavor description demonstrated the feasibility but also the complexity of this task. Additional studies including different structural representations, careful selection of subsets from this data set, as well as the use of a number of classification methods will demonstrate the utility of structure–flavor associations. This work shows that the flavor information contained in databases, such as that used in the present study, can be analyzed following standard chemoinformatics methods. Copyright

A Comparison of the Stability of Beverage Cloud Emulsions Formulated with Different Gum Acacia‐ and Starch‐Based Emulsifiers

The performance of several hydrocolloids (3 gum acacias, 1 modified gum acacia, and 3 modified starches) in stabilizing beverage emulsions and corresponding model beverages was investigated employing different core materials, emulsifier usage levels, and storage temperatures. Concentrated emulsions were prepared using orange terpenes or Miglyol 812 (comprising medium-chain triglycerides, MCT) weighted 1:1 with ester gum, stored at 25 or 35 degrees C, and analyzed on days 0, 1, and 3. On day 3, model beverages were made from each emulsion, stored at both temperatures, and analyzed weekly for 4 wk. Stability of concentrated emulsions was assessed by measuring mean particle size and by visual observations of ringing; beverage stability was judged similarly and also by loss of turbidity. Particle size measurements showed concentrated emulsions containing gum acacia or modified gum acacia with either core material were stable over 3 d storage at both temperatures whereas those made with modified starches were not, destabilization being faster at 35 degrees C. Beverages based on orange terpenes, in contrast to Miglyol, yielded smaller mean particle sizes, both on manufacture and during storage, regardless of hydrocolloid used. Visual observations of ringing generally supported this finding. Modified gum acacia was evaluated at both recommended and higher usage levels, stability increasing in the latter case. In general, all gum acacia and modified gum acacia emulsifiers were superior in stability to those based on modified starches, at either temperature, for orange terpene-based beverages. In Miglyol-based beverages, similar results were seen, except 1 modified starch performed as well as the gum acacia products.

Systematic Mining of Generally Recognized as Safe (GRAS) Flavor Chemicals for Bioactive Compounds

Bioactive food compounds can be both therapeutically and nutritionally relevant. Screening strategies are widely employed to identify bioactive compounds from edible plants. Flavor additives contained in the so-called FEMA GRAS (generally recognized as safe) list of approved flavoring ingredients is an additional source of potentially bioactive compounds. This work used the principles of molecular similarity to identify compounds with potential mood-modulating properties. The ability of certain GRAS molecules to inhibit histone deacetylase-1 (HDAC1), proposed as an important player in mood modulation, was assayed. Two GRAS chemicals were identified as HDAC1 inhibitors in the micromolar range, results similar to what was observed for the structurally related mood prescription drug valproic acid. Additional studies on bioavailability, toxicity at higher concentrations, and off-target effects are warranted. The methodology described in this work could be employed to identify potentially bioactive flavor chemicals present in the FEMA GRAS list.

Computational methods for the discovery of mood disorder therapies

Introduction: Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 – 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators. Areas covered: This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling. Expert opinion: While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.

Chemoinformatics Analysis and Structural Similarity Studies of Food-Related Databases

Chemoinformatics approaches to problem solving are commonly used in both academia and industry, and while a major focus is the pharmaceutical industry, many other sectors of the chemical industry lend themselves to it equally well. The chemoinformatic concepts, thoroughly discussed in Chap. 1 of this book, are general and can also be applied to address problems frequently encountered in food chemistry. A general strategy when applying these computational methods is to replace biological activity by a food-related property, for instance, flavor character or antioxidative activity. In many cases, the representation of the chemical structure remains the same (using, for example, molecular fingerprints, physicochemical and/or structure/substructure representations). In other words, structure/activity relationships (SAR) studies commonly conducted in medicinal chemistry for the purpose of drug discovery can be generalized to the study of structure–property relationships (SPR) for virtually any chemistry-related project. Herein, we discuss representative and specific applications of methods used in chemoinformatics to mine data and characterize SPR information relevant to food chemistry. The chapter is organized into two major sections. First, we discuss exemplary applications of chemoinformatic analyses and characterization of the chemical space of compound databases. In this section, we cover major related concepts such as chemical space and molecular representation. The second section is focused on the application of similarity searching to food chemical databases.

Software and Online Resources: Perspectives and Potential Applications

Chemical databases arose as a tool for the storage of chemical structures and related information. Governmental agencies and others’ initiatives from around the world have compiled chemical databases to serve specific purposes. After compilation, curation, implementation, and launch, the databases are maintained through continuous updates and corrections. Comparative analysis among databases allows for the detection of complementarity, redundancy, or uniqueness. In addition, it provides information about missing areas in property and/or chemical space. This is routinely performed in the drug discovery field, typically based on chemical structures. The translation of these analyses to other areas, such as flavor materials, is emerging. Discussed below are a number of currently available databases of use to those working in the food/flavor area. Representative software and online resources available for the chemoinformatic analysis of such databases is presented in the second section of this chapter. The last section presents the author’s perspectives of the field and potential applications with the intent of motivating the use of chemoinformatic tools to the food chemistry field.