Phytochemical, Geographical, and Pharmacological Retrospect of Genus Torilis.

BACKGROUND: Genus Torilis (Apiaceae) known as hedge parsley, encompasses 11-13 species distributed worldwide and shows potential pharmacological uses. Its phytochemical pattern is highly diversified including many phenolic and terpenic compounds. OBJECTIVE: This research-review provides new highlighting of structural organizations, structure-activity trends, taxonomical, tissue and geographical distribution of phytocompounds of Torilis genus from extensive statistical analyses of available data. METHODS: In extenso, exploration of documented literature and statistical data analyses were applied to update the phytochemical pool of the genus under several aspects including structural diversity, geographical distribution, biological compartmentations and pharmacological activities. RESULTS: Phytoconstituents were classified into homogeneous clusters that revealed to be associated with chemical constitutions (aglycone types, chemical groups) and distributions (through species, tissues, geographical). About bioactivities, terpenes were studied from a pharmacological point of view with relatively high frequencies for antifungal, antibacterial, cytotoxic and anti-inflammatory activities. Preliminary structure-activity relationships were highlighted implying opposite effects between hydroxylation and methylation in favor of different activities. Crude extracts and isolated compounds have shown several biological activities (antibacterial, anticancer, antiangiogenic, antiproliferative, etc.), thus providing authentic scientific proof for their diverse uses in folk medicines. CONCLUSION: The phytochemistry of the genus Torilis promises important perspectives in matters of pharmacological activities. These perspectives call for further investments in pharmacology because of (i) unbalance between phenolic and terpenic compounds according to the countries and (ii) more advanced current states of structural elucidations compared to biological evaluations.

Effect of honey bee forage plants in Tunisia on diversity and antibacterial potential of lactic acid bacteria and bifidobacteria from Apis mellifera intermissa and its products.

Lactic acid bacteria and bifidobacteria (LAB and Bifido), isolated from the gastrointestinal tract of Apis mellifera intermissa (BGIT), honey (H), propolis (P) and bee bread (BB) of hives set in different vegetations (wildflowers, caraway, orange blossom, Marrubium vulgare, Eucalyptus and Erica cinerea), were subjected to analysis of their antibacterial potential. Isolates able to inhibit Staphylococcus aureus were selected and identified with MALDI-TOF MS leading to 154 strains representing 12 LAB and Bifido species. Lactiplantibacillus plantarum, Pediococcus pentosaceus and Enterococcus faecalis were predominantly found in all matrices. BGIT showed the highest LAB and Bifido diversity with exclusive occurrences of five species (including Bifidobacterium asteroides and Limosilactobacillus fermentum). Honey was the second origin harboring an important variety of LAB species of which Apilactobacillus kunkeei and Enterococcus mundtii were characteristic of both H and BGIT. Principal components analysis revealed associations between antibacterial activities of LAB and Bifido, matrices and honey bee forage plants. Inhibition trends of S. aureus and Citrobacter freundii were highlighted with: L. plantarum from BGIT, P, H of bees feeding on E. cinerea; Pediococcus pentosaceus from BGIT, P, BB associated with E. cinerea; and Bifidobacterium asteroides from BGIT/orange blossom system. However, Enterococcus faecium associated with BGIT/Eucalyptus system antagonized Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii. Our findings highlighted noteworthy effects of bee forage plants on the antibacterial activity of LAB and Bifido. Our approach could be useful to identify multiple conditions promoting antibacterial potency of LAB and Bifido under the combined effects of feeding plants and living matrices.

Structure-activity trends analysis between amino acid compositions and minimal inhibitory concentrations of antimicrobial peptides.

Antimicrobial peptides (AMPs) provide large structural libraries of molecules with high variability of constitutional amino acids (AAs). Highlighting structural organization and structure-activity trends in such molecular systems provides key information on structural associations and functional conditions that could usefully help for drug design. This work presents link analyses between minimal inhibitory concentration (MIC) and different types of constitutional AAs of anti-Pseudomonas aeruginosa AMPs. This scope was based on a dataset of 328 published molecules. Regulation levels of AAs in AMPs were statistically ordinated by correspondence analysis helping for classification of the 328 AMPs into nine structurally homogeneous peptide clusters (PCs 1-9) characterized by high/low relative occurrences of different AAs. Within each PC, negative trends between MIC and AAs were highlighted by iterated multiple linear regression models built by bootstrap processes (bagging). MIC decrease was linked to different AAs that varied with PCs: alcohol-type AAs (Thr, Ser) in Cys-rich and low Arg PCs (PCs 1-3); basic AAs (Lys, Arg) in Pro-rich and low Val PCs (PCs 4-8); Trp (heterocyclic AA) in Arg-rich PCs (PCs 6, 7, 9). Aliphatic AAs (more particularly Gly) showed MIC reduction effects in different PCs essentially under interactive forms.

Review on Structural Trends and Chemotaxonomical Aspects of Pharmacologically Evaluated Flavonoids.

INTRODUCTION: This work provides statistical analyses of bibliographic data on pharmacologically evaluated flavonoids from different plant families. By opposition to structural elucidations benefitting from full data aspects, pharmacological evaluations are concerned with partial investigations resulting in sparse information. METHODS: The limited data availability was overcome by extensive consideration of several small sets of pharmacologically evaluated flavonoids in several plant taxa, alternatively to the traditional intensive analysis of big dataset of a given metabolic family in a given plant taxon. Statistical analyses were carried out using correspondence analysis, cluster analysis, box plots and fisher exact test to highlight structure-structure, structure-plant and structure-activity trends. RESULTS: Different aglycone types showed opposite trends between hydroxylation (flavonols, anthocyanidins, flavanols), and methoxylation (isoflavones, isoflavanes, neoflavones). Moreover, different carbons showed differential substitution levels in different aglycones: C3 in flavonols, C6, C8 in flavones, flavonols, C2' in flavanones, C6' in isoflavanes. Plant families were well differentiated by different relative occurrences of aglycones: flavones in Lamiaceae, flavanones in Rutaceae, neoflavones in Rubiaceae, flavonols in Asteraceae, isoflavones in Fabaceae. Relatively more hydroxylated flavonoids occurred in Asteraceae, Lamiaceae and Fabaceae vs. more methoxylated ones in Rutaceae and Rubiaceae. Concerning structure-activity trends, flavanols and isoflavones were relatively more concerned with anti-diabetic and anti-inflammatory activities, respectively, vs. balanced distribution of flavones. Anti-inflammatory activity showed significant association with substitution position of same chemical groups (OH, OCH3), whereas anti-diabetic activity was revealed to be mainly influenced by the type of chemical groups (positive effect of OH and glycosyls). CONCLUSION: These results call for regular updates and further investigations.

Highlighting Aglycone-dependent Glycosylation Aspects in Caryophyllaceae Saponins by a Simplex Simulation Approach.

BACKGROUND: Saponin metabolism shows high structural variability due to the diversity of aglycones and glycosylations (Gly). Although they represent a potential source of drug design, their metabolism remains misunderstood yet due to insufficient investments in analytical methods. AIMS: Bibliographic structural data offer a wide field for extensive statistical analysis, highlighting mechanistic orders governing metabolic diversity. This work presents an original simulation method based on simplex rule for highlighting regulatory mechanisms of metabolism from categorical structural data. METHODS: Simulation was applied on a set of 231 saponins of the Caryophyllaceae plant family initially affiliated to four aglycone types: gypsogenin (Gyp), quillaic acid (QA), gypsogenic acid (GA), and 16-OH-gypsogenic acid (16-OH-GA). Molecules were initially characterized by relative glycosylation levels of different carbons. Simplex approach was applied by combining saponins of the four aglycone groups using a complete set of N gradual weightings between structural groups. In silico combinations were applied by randomly sampling representative saponins from the four groups conforming to their weights given by mixture design. Gly profiles of sampled saponins were averaged to calculate a barycentric molecular profile for each mixture. With N mixtures, N barycentric molecules were iteratively calculated by bootstrap, leading to smoothed data from which Gly trends between carbons were highlighted. RESULTS: Sequential, competing and cooperative Gly trends were highlighted according to the types of aglycones, attached saccharides and positions of substituted carbons. Such various conditional Gly trends seemed to be linked to multiple factors, including steric effects, regio-selectivity, enzymatic specificity and enzymatic promiscuity. These simulated results could be helpfully useful in chemical synthesis and drug design. CONCLUSION: These simulated results could usefully help for chemical syntheses and drug design.

Measurement of total phenolic content and antioxidant activity of aerial parts of medicinal plant Coronopus didymus.

OBJECTIVE: To evaluate the total phenolic content and compare the antioxidant activity of various solvent extracts and fractions from the aerial parts of Coronopus didymus through various assays. METHODS: Total phenolic content was determined using the Folin-Ciocalteu assay and the in vitro antioxidant activity of a number of different extracts was investigated in a dose-dependent manner with three different methods: the 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS) and ferric reducing antioxidant power (FRAP) assays. A flavone was isolated from the most active ethanolic extract with high antioxidant activity using size exclusion chromatography. IC50 values were calculated for the DPPH and ABTS methods. The FRAP activity was assessed in terms of µM Fe (II) equivalent. RESULTS: The phenolic content was found to be highest in the ethanol extract (CDA Et; 47.8 mM GAE) and the lowest in the dichloromethane extract (CDA DCM; 3.13 mM GAE). The ethanol extract showed high radical scavenging activity towards DPPH and ABTS radicals with IC50 values of (7.80 × 102) and (4.32 × 102) µg/mL, respectively. The most active ethanol extract had a FRAP value of 1921.7 µM Fe (II) equivalent. The isolated flavone F10C (5,7,4'-trihydroxy-3'-methoxy flavone) was far more effective for scavenging free radicals in the DPPH and ABTS assays with IC50 of 43.8 and 0.08 µg/mL, than the standard trolox, with IC50 values of 97.5 and 21.1 µg/mL, respectively. In addition, the flavone F10C and the standard ascorbic acid had FRAP values of 1621.7 and 16 038.0 µM Fe (II) equivalents, respectively. CONCLUSIONS: The total phenolic content of extracts in decreasing order is ethanol extract (CDA Et) > acetone extract (CDA ACE) > phenolic extract (CDA MW) > n-hexane extract (CDA nHX)> chloroform extract (CDA CHL) > dichloromethane extract (CDA DCM). The ordering of extracts in terms of antioxidant activity from highest to lowest is CDA Et > CDA MW > CDA DCM > CDA CHL > CDA ACE > CDA nHX in DPPH, ABTS and FRAP assays. A significant relationship is found between antioxidant potential and total phenolic content, suggesting that phenolic compounds are the major contributors to the antioxidant activity of C. didymus.

Chemometric Analysis Of Inter- And Intra-Molecular Diversification Factors By A Machine Learning Simplex Approach. A Review And Research On Astragalus Saponins.

Metabolisms represent highly organized systems characterized by strong regulations satisfying the mass conservation principle. This makes a whole chemical resource to be competitively shared between several ways at both intra-and inter-molecular scales. Whole resource sharing can be statistically associated with a constant sum-unit constraint which represents the basis of simplex mixture rule. In this work, a new simplex-based simulation approach was developed to learn scaffold information on metabolic processes controlling molecular diversity from a wide set of observed chemical structures. Starting from a dataset of chemical structures classified into p clusters, a machine learning process was applied by linearly combining the p clusters j and randomly sampling a constant number (n) of molecules according to different clusters' weights (wj/w) given by Scheffé's mixture matrix. At the output of mixture design, molecular linear combinations lead to calculate barycentric molecules integrating the characteristics of the different weighted clusters. The N mixtures-design was iterated by bootstrap technique leading to extensive exploration of chemical variability between and within clusters. Finally, the K response matrices issued from K iterated mixture designs were averaged to calculate a smoothed matrix containing scaffold information on regulation processes responsible for molecular diversification at inter- and intra-molecular (atomic) scales. This matrix was used as a backbone for graphical analysis of positive and negative trends between atomic characteristics (chemical substitutions) at both mentioned scales. This new simplex approach was illustrated by cycloartane-based saponins of Astragalus genus by combining three desmosylation clusters characterized by relative glycosylation levels of different aglycones' carbons.

An updated review on pharmacological activities and phytochemical constituents of evening primrose (genus Oenothera)

Genus Oenothera includes medicinal plants that are distributed throughout the world and are known since ancient times.Popular indications of different species of this genus include treatment of inflammations,diabetes,microbial infections,ulcers,tumors,kidney and liver problems.The plants of this genus are a botanical source for various pharmaceutically active components like sterols,alkaloids,phenolic acids,flavonoids,tdterpenoids,saponins,bitlavonols and tocopherols.This review article is a compilation of chemical composition and biological activities of the various species of the genus Oenothera.

Chemometrics Methods for Specificity, Authenticity and Traceability Analysis of Olive Oils: Principles, Classifications and Applications.

BACKGROUND: Olive oils (OOs) show high chemical variability due to several factors of genetic, environmental and anthropic types. Genetic and environmental factors are responsible for natural compositions and polymorphic diversification resulting in different varietal patterns and phenotypes. Anthropic factors, however, are at the origin of different blends' preparation leading to normative, labelled or adulterated commercial products. Control of complex OO samples requires their (i) characterization by specific markers; (ii) authentication by fingerprint patterns; and (iii) monitoring by traceability analysis. METHODS: These quality control and management aims require the use of several multivariate statistical tools: specificity highlighting requires ordination methods; authentication checking calls for classification and pattern recognition methods; traceability analysis implies the use of network-based approaches able to separate or extract mixed information and memorized signals from complex matrices. RESULTS: This chapter presents a review of different chemometrics methods applied for the control of OO variability from metabolic and physical-chemical measured characteristics. The different chemometrics methods are illustrated by different study cases on monovarietal and blended OO originated from different countries. CONCLUSION: Chemometrics tools offer multiple ways for quantitative evaluations and qualitative control of complex chemical variability of OO in relation to several intrinsic and extrinsic factors.

Review and research on feature selection methods from NMR data in biological fluids. Presentation of an original ensemble method applied to atherosclerosis field.

Metabolic pools of biological matrices can be extensively analyzed by NMR. Measured data consist of hundreds of NMR signals with different chemical shifts and intensities representing different metabolites' types and levels, respectively. Relevant predictive NMR signals need to be extracted from the pool using variable selection methods. This paper presents both a review and research on this metabolomics field. After reviews on discriminant potentials and statistical analyses of NMR data in biological fields, the paper presents an original approach to extract a small number of NMR signals in a biological matrix A (BM-A) in order to predict metabolic levels in another biological matrix B (BM-B). Initially, NMR dataset of BM-A was decomposed into several row-column homogeneous blocks using hierarchical cluster analysis (HCA). Then, each block was subjected to a complete set of Jackknifed correspondence analysis (CA) by removing separately each individual (row). Each CA condensed the numerous NMR signals into some principal components (PCs). The different PCs representing the (n - 1) active individuals were used as latent variables in a stepwise multi-linear regression to predict metabolic levels in BM-B. From the built regression model, metabolite level in the outside individual was predicted (for next model validation). >From all the PCs-based regression models resulting from all the jackknifed CA applied on all the individuals, the most contributive NMR signals were identified by their highest absolute contributions to PCs. Finally, these selected NMR signals (measured in BMA) were used to build final population and sub-population regression models predicting metabolite levels in BM-B.

A new simplex approach to highlight multi-scale feeding behaviors in forager species from stomach contents: application to insectivore lizard population.

Stomach contents represent complex mixture systems which depend on feeding mode and level of forager species (carnivores, herbivores) as well as on natural availability/distribution of food resources (preys, plants). Such mixture systems can be considered as small black boxes condensing wide ecological information on (i) feeding behaviors of predator (or herbivore) and (ii) local diversity of preys (or host plants). Feeding behaviors of a hunter species toward different prey taxa show a complex variability whose investigation requires multivariate statistical tools. This paper presents a new computational approach which statistically analyzes stomach contents' variability in a predator population to graphically highlight different feeding behaviors. It is a simulation approach iteratively combining the variability of different diet patterns by using a simplex mixture design. Average combinatorial results are graphically visualized to highlight scale-dependent relationships between consumption rates of different food types found in the stomachs. The simplex approach was applied on different subpopulations of Phrynosoma douglassi brevirostre, an insectivore lizard species. These subpopulations were initially defined by different criteria including statistical clusters, gender and sampling periods. Results highlighted successive trade-offs over months of captured potential preys switching from small and less mobile preys to large and flying ones. In these dietary transitions, P. douglassi manifested a systematic memorization of previous preys and a gradual foraging learning of the next ones. These results highlighted lightness on dietary flexibility helping this specialist predator to switch between diets based on different potential preys. Adult male and adult female lizards showed different feeding behaviors due to some predation lag-time between them and different dietary ratios toward the same considered preys.

Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts.

Complexity of metabolic systems can be undertaken at different scales (metabolites, metabolic pathways, metabolic network map, biological population) and under different aspects (structural, functional, evolutive). To analyse such a complexity, metabolic systems need to be decomposed into different components according to different concepts. Four concepts are presented here consisting in considering metabolic systems as sets of metabolites, chemical reactions, metabolic pathways or successive processes. From a metabolomic dataset, such decompositions are performed using different mathematical methods including correlation, stiochiometric, ordination, classification, combinatorial and kinetic analyses. Correlation analysis detects and quantifies affinities/oppositions between metabolites. Stoichiometric analysis aims to identify the organisation of a metabolic network into different metabolic pathways on the hand, and to quantify/optimize the metabolic flux distribution through the different chemical reactions of the system. Ordination and classification analyses help to identify different metabolic trends and their associated metabolites in order to highlight chemical polymorphism representing different variability poles of the metabolic system. Then, metabolic processes/correlations responsible for such a polymorphism can be extracted in silico by combining metabolic profiles representative of different metabolic trends according to a weighting bootstrap approach. Finally evolution of metabolic processes in time can be analysed by different kinetic/dynamic modelling approaches.

A new mixture design-based approach to graphical screening of potential interconnections and variability processes in metabolic systems.

The flexibility of metabolic systems implies a high variability of metabolic profiles linked to different regulation ratios between metabolites. Such regulations are controlled by several interactive metabolic pathways resulting in multidirectional continuums of metabolic profiles. This article presents a new metabolomic approach helping to graphically analyse the flexibility of metabolic regulation systems. Its principle consists in extracting a metabolic backbone from iterative combinations of metabolic profiles representing different metabolic trends. The iterated combinations were performed on the basis of Scheffe matrix then averaged to calculate a response matrix of smoothed metabolic profiles. From such a smoothed matrix, a graphical analysis of relationships between metabolites highlighted different scale-dependent variation paths responsible for the observed metabolic trends. Such a flexibility favouring some metabolites at the expense of others was indirectly checked by a single kinetic approach by considering both the variation of maximal concentrations and the metabolic trends in time. This kinetic approach highlighted a succession of metabolic trends linked to the variation of maximal concentrations in time. Finally, a delayed regulation of a metabolite was highlighted both by the kinetic approach and by a dynamic application of the metabolomic approach. This new approach was illustrated on a dataset of blood concentrations of levodopa and its metabolites analysed in 34 patients at different times.

Independent-model diagnostics for a priori identification and interpretation of outliers from a full pharmacokinetic database: correspondence analysis, Mahalanobis distance and Andrews curves.

Population pharmacokinetic (PK) (or pharmacodynamic (PD)) modelling aims to analyse the variability of drug kinetics (or dynamics) between numerous subjects belonging to a population. Such variability includes inter- and intra-individual sources leading to important differences between the variation ranges, the relative concentrations and the global shapes of PK profiles. These various sources of variability suggest that the distance metrics between the subjects can be examined under different aspects. Some subjects are so distant from the majority that they tend to be atypical or outliers. This paper presents three multivariate statistical methods to diagnose the outliers within a full population PK dataset, prior to any modelling step. Each method combined all the concentration-time variables to analyse the differences between patients by referring to a distance criterion: (a) Correspondence analysis (CA) used the chi-square distance to highlight the most atypical profiles in terms of relative concentrations; (b) Mahalanobis distance was calculated to extract PK profiles showing atypical shapes due to atypical variations in concentration; (c) Andrews method combined all the concentration variables into a Fourier transformation to give sine-cosine curves showing the clustering behaviours of subjects under the Euclidean distance criterion. After identification of outlier subjects, these methods can also be used to extract the concentration values which cause the atypical states of the patients. Therefore, the outliers will incorporate different variability sources of the PK dataset according to each method and independently of any PK modelling. Finally, a significant positive trend was found between the number of times outlier concentrations were detected (by one, two or three diagnostics) and the NPDE metrics of these concentrations (after a PK modelling): NPDE were highest when the corresponding concentration was detected by more diagnostics a priori. The application of a priori outlier diagnostics is illustrated here on two PK datasets: stimulated cortisol by synacthen and capecitabine administrated orally.

A new oleanane glycoside from the roots of Astragalus caprinus.

A novel oleanane-type triterpene saponin (1) together with two known molecules, soyasapogenol B and astragaloside VIII were isolated from the roots of Astragalus caprinus. Their structural elucidation was performed mainly by 2D NMR techniques (COSY, TOCSY, NOESY, HSQC, HMBC) and mass spectrometry. Compound 1 was determined as 3-O-[alpha-L-rhamnopyranosyl-(1 --> 2)-beta-D-glucuronopyranosyl]-22-O-beta-D-apiofuranosyl-soyasapogenol B.

Review in pharmacokinetic models on corticosteroids.

The pharmacokinetics of corticosteroids provides a large set of mathematical models which led to analyse many kinetic profiles corresponding to many clinical and/or physiological situations. In this paper, we present a review on the usefulness, advantages and limits of such models which could find a large application in medicinal chemistry.

Cluster analysis: an alternative method for covariate selection in population pharmacokinetic modeling.

To be analyzed, the heterogeneity characterizing biological data calls for using appropriate models involving numerous variables. A high variable number could become problematic when one needs to determine a priori the most significant variable combination in order to reduce the inter-individual variability (IIV). Alternatively to multiple introductions of single variables, we propose a single introduction of a multivariate variable. We present cluster analysis as a stratification strategy that combines the initial single covariates to build a multivariate categorical covariate. It is an exploratory multivariate analysis that outlines homogeneous categories of individuals (clusters) according to similarities from the set of covariates. It includes many clustering techniques combining a distance measure and a linkage algorithm, and leading to various stratification patterns. The cluster analysis approach is illustrated by a case study on cortisol kinetics in 82 patients after intravenous bolus administration of synacthen (synthetic corticotropin). Using NONMEM, a basic infusion model was initially achieved for cortisol, and then a classical covariate selection was applied to improve IIV. The best fit was between the elimination rate constant k and the body mass index (BMI), which improved IIV of k. An alternative method is presented consisting in the population into homogeneous and non-overlapping groups by applying a cluster analysis. Such categorization (or clustering) was carried out using Euclidean distance and complete-linkage algorithm. This algorithm gave five dissimilar clusters that differed by increasing BMI, obesity duration, and waist-hip ratio. The dispersion of k according to the five clusters showed three distinctvariation ranges a priori, which corresponded a posteriori(after NONMEM modeling) to three sub-populations of k. After grouping the clusters that had similar variation ranges of k, we obtained three final clusters representing non-obese, intermediate, and extreme obese sub-populations. The pharmacokinetic model based on three clusters was better than the basic model, similar to the classical covariate model, but had a stronger interpretability: It showed that the stimulation and elimination of cortisol were higher in the extreme obese followed by intermediate then non-obese subjects.

New flavonol tetraglycosides from Astragalus caprinus.

A new flavonol tetraglycoside, together with four acylated derivatives, were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods, mainly 2D NMR, as kaempferol-3-O-[[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)][alpha-L-rhamnopyranosyl(1-->2)]]-beta-D-galactopyranoside (1), its 3(Gal)-p-coumaric (2) and 3(Gal)-ferulic (3) esters, and its 4(Gal)-p-coumaric (4) and 4(Gal)-ferulic (5) esters.

Four new flavonol glycosides from the leaves of Astragalus caprinus.

Four new flavonol 3-O-glycosides were isolated from the leaves of Astragalus caprinus. Their structures were elucidated by spectroscopic methods as rhamnocitrin-3-O-[3-hydroxy-3-methylglutaroyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (1), rhamnetin-3-O-[3-hydroxy-3-methylglutaroyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (2), kaempferol-3-O-[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)]-beta-D-galactopyranoside (3), and quercetin-3-O-[beta-D-xylopyranosyl(1-->3)-alpha-L-rhamnopyranosyl(1-->6)][beta-D-apiofuranosyl(1-->2)]-beta-D-galactopyranoside (4).