A new electronic-topological investigation of the relationship between chemical structure and ambergris odour

Mammalian olfactory receptors: molecular mechanisms of odorant detection, 3D-modeling, and structure-activity relationships

This chapter describes the main characteristics of olfactory receptor (OR) genes of vertebrates, including generation of this large multigenic family and pseudogenization. OR genes are compared in relation to evolution and among species. OR gene structure and selection of a given gene for expression in an olfactory sensory neuron (OSN) are tackled. The specificities of OR proteins, their expression, and their function are presented. The expression of OR proteins in locations other than the nasal cavity is regulated by different mechanisms, and ORs display various additional functions. A conventional olfactory signal transduction cascade is observed in OSNs, but individual ORs can also mediate different signaling pathways, through the involvement of other molecular partners and depending on the odorant ligand encountered. ORs are engaged in constitutive dimers. Ligand binding induces conformational changes in the ORs that regulate their level of activity depending on odorant dose. When present, odorant binding proteins induce an allosteric modulation of OR activity. Since no 3D structure of an OR has been yet resolved, modeling has to be performed using the closest G-protein-coupled receptor 3D structures available, to facilitate virtual ligand screening using the models. The study of odorant binding modes and affinities may infer best-bet OR ligands, to be subsequently checked experimentally. The relationship between spatial and steric features of odorants and their activity in terms of perceived odor quality are also fields of research that development of computing tools may enhance.

Disila-Okoumal: A Silicon Analogue of the Ambergris Odorant Okoumal

Two-fold sila-substitution (C/Si exchange) in the saturated ring of the tetrahydronaphthalene skeleton of the ambery odorant okoumal (5) provides disila-okoumal (6). The okoumal isomers 5 a-d were synthesized from 1-(5,5,8,8-tetramethyl-5,6,7,8-tetrahydro-2-naphthyl)ethanone (7), and the silicon analogues 6 a-d were synthesized from 1-(5,5,8,8-tetramethyl-5,8-disila-5,6,7,8-tetrahydro-2-naphthyl)ethanone (8). Detailed olfactory properties of 5 a-d and 6 a-d are reported, together with the respective threshold values. All enantiomers of okoumal and disila-okoumal exhibit typical ambery odor notes with woody facets, as is characteristic of okoumal and karanal, but a stereocenter at the 2-position was found to be of utmost importance for the odor thresholds; the lowest value of 0.31 ng per L air was measured for the 2R-configured silicon compounds 6 a and 6 c.

New woody and ambery notes from cedarwood and turpentine oil

The development of a new product in the chemical industry is still driven by needs like technical properties, price/performance ratio, biodegradability, or product safety. However, in terms of improving more and more on ecological criteria, summarized under such catchphrases as sustainable development or green chemistry, another important aspect is to use renewable resources as starting materials. This is not significantly new in fragrance chemistry, and there are a lot of raw materials in the perfume oils that are derived from molecules of renewable resources. Two commonly used materials are: longifolene (from turpentine oil) and cedrene (from cedarwood oil). These compounds are very suitable for the synthesis of woody and ambery notes, and even though it seemed that all possibilities were exhausted, it is actually still feasible to discover new molecules with excellent olfactory properties such as Ambrocenide (50a), which is available in three steps from alpha-cedrene. Some of these molecules will be treated in this review, both with respect to synthesis as well as structural and sensory aspects.

Combinatorial QSAR of Ambergris Fragrance Compounds

A combinatorial quantitative structure-activity relationships (Combi-QSAR) approach has been developed and applied to a data set of 98 ambergris fragrance compounds with complex stereochemistry. The Combi-QSAR approach explores all possible combinations of different independent descriptor collections and various individual correlation methods to obtain statistically significant models with high internal (for the training set) and external (for the test set) accuracy. Seven different descriptor collections were generated with commercially available MOE, CoMFA, CoMMA, Dragon, VolSurf, and MolconnZ programs; we also included chirality topological descriptors recently developed in our laboratory (Golbraikh, A.; Bonchev, D.; Tropsha, A. J. Chem. Inf. Comput. Sci. 2001, 41, 147-158). CoMMA descriptors were used in combination with MOE descriptors. MolconnZ descriptors were used in combination with chirality descriptors. Each descriptor collection was combined individually with four correlation methods, including k-nearest neighbors (kNN) classification, Support Vector Machines (SVM), decision trees, and binary QSAR, giving rise to 28 different types of QSAR models. Multiple diverse and representative training and test sets were generated by the divisions of the original data set in two. Each model with high values of leave-one-out cross-validated correct classification rate for the training set was subjected to extensive internal and external validation to avoid overfitting and achieve reliable predictive power. Two validation techniques were employed, i.e., the randomization of the target property (in this case, odor intensity) also known as the Y-randomization test and the assessment of external prediction accuracy using test sets. We demonstrate that not every combination of the data modeling technique and the descriptor collection yields a validated and predictive QSAR model. kNN classification in combination with CoMFA descriptors was found to be the best QSAR approach overall since predictive models with correct classification rates for both training and test sets of 0.7 and higher were obtained for all divisions of the ambergris data set into the training and test sets. Many predictive QSAR models were also found using a combination of kNN classification method with other collections of descriptors. The combinatorial QSAR affords automation, computational efficiency, and higher probability of identifying significant QSAR models for experimental data sets than the traditional approaches that rely on a single QSAR method.

Odds and Trends: Recent Developments in the Chemistry of Odorants Note on trademarks: Words which we know or have reason to believe constitute registered trademarks (R) are designated as such. However, neither the presence nor absence of such designation should be regarded as affecting the legal status of any trademark. Note on perfume analysis: The quoted percentages of perfume raw materials in market products are rounded figures. They are often derived from area percentages from the GC (FID) analysis, and are thus subject to analytical error

Fragrance chemistry is, together with the closely related area of flavor chemistry, one of the few domains, if not the only one, in which chemists can immediately experience structure-activity relationships. This review presents structure-odor correlations and olfactophore models for the main odor notes of perfumery: "fruity", "marine", "green", "floral", "spicy", "woody", "amber", and "musky". New trendsetters and so-called captive odorants of these notes are introduced, and recent activities and highlights in fragrance chemistry are summarized. The design of odorants, their chemical synthesis, and their use in modern perfumery is discussed. Our selection is guided and illustrated by creative fragrances, and features new odorants which encompassed current trends in perfumery. New odorants for grapefruit and blackcurrant, for galbanum, and leafy top notes are presented. Compounds with fashionable marine, ozonic, and aquatic facets are treated, as well as new odorants for classical lily-of-the-valley, rose, and jasmine accords. Compounds with sweet and spicy tonalities are also discussed, as are the most recent developments for woody notes such as sandalwood and vetiver. We conclude with musky and ambery odorants possessing uncommon or unusual structural features. Some odor trends and effects are illustrated by microencapsulated fragrance samples, and areas where there is need for the development of new synthetic materials and methodologies are pointed out. Thus, chemists are invited to explore fragrance chemistry and participate in the design and synthesis of new odorants. This review gives the latest state of the art of the subject.