Compound intake estimator

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Compound intake estimator calculates intakes of compounds based on food or food supplement intake, compound concentration in food, and guidance values for the compound.

Question

How to estimate intakes and compare them to guidance values?

Answer

The concern indicator is calculated for each compound separately. If the intake is smaller than the threshold of concern, the indicator is < 1 and there is no concern. The threshold of concern is acceptable daily indake (ADI), benchmark dose (BMDL10), or threshold of toxicological concern (TTC), depending on what data is available for each compound.

Intake information Fraction of essential oil in the supplement (%): Amount of supplement per day (g /d): Show intermediate results?: No Yes Do you want to add own compound data?: No Yes Additional compound information Name(s) of additional compound(s): Concentration(s) of the compound(s) (mg of compound /g product): + Show code- Hide code library(OpasnetUtils) library(ggplot2) objects.latest("Op_en6007", code_name = "answer") # Fetch official draft objects from [[OpasnetUtils/Drafts]] (in this case, ograph) ################## USER INPUTS N <- 10 name <- "Cinnamon essential oil" # name is the name of product. It is used in the output graphs. # food.amount is the amount that the consumer consumes the defined product (g /day) food.amount <- Ovariable("food.amount", data = data.frame(Result = food.amount)) # fr.const is the fraction of a critical constituent in the food or food supplement (given as percentage) fr.const <- Ovariable("fr.const", data = data.frame(Result = fr.const)) # extraction is a list of extraction techniques to consider. (by default, all extraction techniques) extraction <- Ovariable("extraction", data = data.frame(Result = 1)) # addcomp is user-defined additional compounds for a supplement, if they are not in the table. ## addcompound is the name and addconc is the concentration of the additional compound. addcompound <- ifelse(! exists('addcompound') || addcompound == ' ', NA, addcompound) addconc <- ifelse(! exists('addconc') || addconc == ' ', 0, addconc) addcomp <- Ovariable("addcomp", data = data.frame(Compound = addcompound, Result = addconc)) # compound.conc is the concentration of compounds in the critical constituent compound.conc <- Ovariable(name = "compound.conc", ddata = "Op_en6193.example_data") ############## ACTUAL OUTPUT MODEL objects.latest("Op_en6193", code_name="initiate") # Fetches the ovariables needed in the model. # Ovariables downloaded: guidance.values, compound.intake, food.risk food.risk <- EvalOutput(food.risk, N = N) if(intermediates) { cat("Concentrations of compounds in ", name, " (mg /kg DW)\n") oprint(summary(compound.conc, marginals = c("Compound"))) cat("Intakes of different compounds (mg /kg BW /d)\n") oprint(summary(compound.intake, marginals = c("Compound")), digits = 3) ograph(compound.conc, x = "Compound") ograph(compound.intake, x = "Compound") } cat("Risks relative to guidance values (ADI, TDI, BMDL10, or TTC). Values < 1 are of minimal concern and are not shown in the graph.\n") oprint(summary(food.risk, marginals = c("Compound"))) test <- unique(food.risk@output[food.risk@output$food.riskResult >= 1 , "Compound"]) temp <- food.risk@output[food.risk@output$Compound %in% test , ] if(nrow(temp) == 0) { cat("There are no compounds that exceed a guidance value.\n") stop("There are no compounds that exceed a guidance value.\n") } ggplot(temp, aes(x = Compound, y = food.riskResult, fill = Guidance)) + geom_boxplot() + theme_grey(base_size = 24) + labs(title = paste("Exposure to compounds in", name), y = "Ratio (Exposure /Guidance)") + theme(axis.text.x = element_text(angle = 90, hjust = 1))

Example data(%)

Obs	Extraction.technique	Compound	Result	Reference
1	Superheated water extraction, 100 °C	2-Carene	0.08; 0.1; 0.12	Jayawardena & Smith, 2010
2	Superheated water extraction, 100 °C	Z-Cinnamaldehyde	1.63; 2.1; 2.57	Jayawardena & Smith, 2010
3	Superheated water extraction, 100 °C	Cinnamaldehyde	81.14; 83.7; 86.26	Jayawardena & Smith, 2010
4	Superheated water extraction, 100 °C	Eugenol	0.45; 0.8; 1.15	Jayawardena & Smith, 2010
5	Hydrodistillation	1,8-Cineole	0.2	Chericoni et al, 2005
6	Superheated water extraction, 100 °C	Cinnamyl acetate	5.47; 7.2; 8.93	Jayawardena & Smith, 2010

Rationale

The idea of the tool is to look at consumption of a particular PFS product, and estimate whether any of the compounds in the product cause concern. The estimation follows this equation:

compound intakes = product intake (g /day) * concentration of each compound in the product (mg /g) / 60
    (60 kg is the assumed body weight of the person)

concern indicator = compound intakes (mg /kg /d) / threshold of concern (mg /kg /d)

Calculations

You need to run the code below only if you update the data tables on this page.

+ Show code- Hide code library(OpasnetUtils) ################## PRE-DEFINED INPUTS # food.conc is a temporary object about the concentration of a compound in food. # compound.conc is the concentration of a compound in the critical constituent (or food, if fr.const == 1) # fr.const is the fraction of the critical constituent in the whole food # extraction is a criteria to select specific extraction techniques # addcomp is an additional compound not in the product. Data about its concentration in food is given by the user. # compound.intake is the intakes of the compounds via food or food supplement, each given as mg /kg /d. compound.intake <- Ovariable("compound.intake", dependencies = data.frame(Names = c("food.amount", "compound.conc", "fr.const", "extraction", "addcomp")), formula = function(...) { food.conc <- compound.conc * 10 * fr.const / 100 # compound.conc from w/w% to mg/g; fr.const from % to fraction food.conc <- food.conc * extraction # remove extractions that are not considered. # addcomp must not contain columns that are not in food.conc. It is multiplied by 1 to make # the result column name "Result". food.conc@output <- orbind(food.conc, addcomp * 1) test <- colnames(food.conc@output) != "Extraction.technique" food.conc@marginal <- food.conc@marginal[test] food.conc@output <- food.conc@output[test] # unit: mg /g out <- food.amount / 60 * food.conc # scale to 60 kg person return(out) } ) # guidance.values is the guidance value (ADI, BMDL10, TTC or similar) for compounds. unit: mg /kg /d # cramer is cramer classes of compounds # cramer.values are the thresholds of toxicological concern for each Cramer class cramer <- tidy(opbase.data("Op_en6193", subset = "cramer_classes_of_compounds")) cramer$Result <- NULL cramer$Guidance <- "TTC" # Threshold for toxicological concern cramer.values <- tidy(opbase.data("Op_en6193", subset = "cramer_values")) cramer.values <- merge(cramer, cramer.values) cramer.values[["Cramer class"]] <- NULL cramer.values$Result <- cramer.values$Result / 1000 # from ug /kg /d to mg /kg /d guidance.values <- tidy(opbase.data("Op_en6193", subset = "guidance_values")) guidance.values <- orbind(cramer.values, guidance.values) guidance.values <- Ovariable("guidance.values", data = guidance.values) # food.risk calculates "hazard quotients" or risks relative to guidance values. Value < 1 are of minimal concern food.risk <- Ovariable(name = "food.risk", dependencies = data.frame(Name = c("compound.intake", "guidance.values")), formula = function(...) { # guidance.risk is intake compared with guidance values. out <- compound.intake / guidance.values removepoorguidance <- function(dat, goodg) { # Remove inferior guidance values # Compounds that have good guidance values (goodg). hasgoodg <- dat[dat$Guidance == goodg , "Compound"] # The row contains data about a chemical with a good guidance value # but the data is about an inferior guidance value. out <- dat[!(dat$Compound %in% hasgoodg & dat$Guidance != goodg) , ] return(out) } out <- removepoorguidance(out@output, "ADI") out <- removepoorguidance(out, "TDI") out <- removepoorguidance(out, "BMDL10") return(out) } ) objects.store(compound.intake, guidance.values, food.risk) cat("The following objects were successfully stored: compound.intake, guidance.values, food.risk\n")

Using different data on different layers: [1].

Data about guidance values

Guidance values(mg /kg-BW /d)

Obs	Compound	Guidance	Result
1	1,8-Cineole	ADI	2.8
2	4-Terpineol	ADI	1.2
3	Benzylbenzoate	ADI	5
4	Cinnamaldehyde	ADI	0.7
5	Coumarin	TDI	0.1
6	Estragole	BMD10	3.3-6.5
7	Eugenol	ADI	2.5
8	Fenchone	ADI	10.64
9	Limonene	ADI	1
10	Linalool	ADI	0.5
11	Myrcene	ADI	2.5
12	Trans-anethole	ADI	2
13	Safrole	BMDL10	1.9-5.1
14	α-Terpineol	ADI	1.2

Cramer values(µg /kg-BW /d)

Obs	Cramer class	Threshold of toxicological concern	Description
1	1	30
2	2	1.5
3	3	1.5
4	4	0.0025	This comes from Apiole?! Is this correct?

Values 30 and 1.5 µg /kg /d come from EFSA (2012)

[show]Cramer classes of compounds

Cramer classes of compounds(-) Obs Compound Cramer class Notes 1 1,8-Cineole 3 A new line was created for 1.8-Cineole based on "ß-Phellandrene + 1,8-Cineole: 1+3" 2 2-Carene 1 3 2-Methoxycinnamaldehyde 1 4 3-Phenylpropyl acetate 1 5 4-Methoxyphenylacetone 1 6 4-Terpineol 3 7 Allo-ocimene 1 8 Apiole 4 This is 0.0025 ug/kg/d!? Is this Cramer class at all? 9 Aromadendrene 1 10 Aromatic compound Class could not be assigned 11 Benzaldehyde 1 12 Benzenepropanal 1 13 Benzenepropanol acetate 1 14 Benzenepropanol 1 15 Benzylbenzoate 1 16 Borneol 1 17 Cadinene 1 18 Calamenene 2 19 Camphene 1 20 Camphor 3 21 Carvacrol 1 22 Carveol isomere 1 Carveol (2-methyl-5-prop-1-en-2-ylcyclohex-2-en-1-ol) is 1. Isomere is not specified so the class was assigned to the general compound 23 Caryophyllene alcohol 3 24 Caryophyllene oxide 3 25 Caryophyllene 1 26 Chavicol 1 27 Cinnamaldehyde 1 28 Cinnamic acid 1 29 Cinnamyl acetate 1 30 Cinnamyl alcohol 1 31 Cis-anethole 2 32 Cis-limonene oxide 2 33 Cis-P-menth-2-en-1-ol 3 34 Cis-sabinene hydrate 2 35 Cis-ß-Ocimene 1 36 Cis-α-ocimene 1 37 Cis-β-ocimene 1 38 Coumarin 3 39 Cryptone 2 40 Cymene isomere 1 cymene is 1. Isomere is not specified so the class was assigned to the general compound 41 E-Anethole 1 42 e-Cadinene + Pioncarvone Class could not be assigned 43 E-Cinnamyl acetate 1 44 E-o-Methoxycinnamaldehye 1 45 Epoxy-6.7-Humulene + Mw=220 Class could not be assigned 46 Estragole 1 Test compound, not real value 47 Eugenol acetate 3 48 Eugenol 1 49 Fenchyl acetate 1 50 Humulene epoxide II 3 51 Isoledene 1 52 Limonene 1 53 Linalool 3 54 Methoxy eugenol 3 55 Methyl cinnamaldehyde 1 56 Methyl eugenol 1 57 Monoterpenes Class could not be assigned 58 N-Hexadecanoic acid 1 59 N-Octadecanoic acid 1 60 o-Cymene 1 61 p-Anisaldehyde 1 62 Para-Methoxy cinnamic aldehyde 1 63 p-Cymene 1 64 P-Cymene-8-ol 3 65 Phenyl cyclohexene 2 66 Phenylmethyle isovalerate 1 67 Rutin 1 Test compound, not real value 68 Sabinene 1 69 Safrole 3 70 Sesquiterpene epoxide Class could not be assigned. Sesquierpene is class 3, but don't now if something changes with epoxide 71 Sesquiterpenol Mw=220 Class could not be assigned 72 Sesquiterpenol Mw=222 Class could not be assigned 73 Spathulenol 3 74 ß-Bisabolene 1 75 ß-Cubebene 1 76 ß-Elemene 1 77 ß-Myrcene 1 78 ß-Phellandrene 1 ß-Phellandrene + 1,8-Cineole: 1+3 79 ß-Pinene 1 80 Styrene 1 81 Terpinen-4-ol 3 82 Terpinolene isomere 1 terpinolene is 1 , isomere not known. Isomere is not specified so the class was assigned to the general compound 83 Terpinolene 1 84 Tetradecanal 1 85 Trans-2-methoxycinnamaldehyde 1 86 Trans-limonene oxide 2 87 Trans-pinene hydrate 2 88 Trans-Pinocarveol 1 89 Trans-Piperitol 1 90 Trans-P-menth-2-en-1-ol 3 91 Trans-ß-Ocimene 1 92 Z-Cinnamaldehyde 1 93 α-Cadinol 3 94 α-Calacorene 2 95 α-Copaene 1 96 α-Cubebene 1 97 α-Famesene Class could not be assigned 98 α-Gurjunene 1 99 α-Humulene 1 100 α-Muurolene 1 101 α-Muurolol 3 102 α-P-Dimethylstyrene 1 103 α-Phellandrene 1 104 α-Pinene 1 105 α-Selinene 1 106 α-Terpinene 1 107 α-Terpineol 3 108 α-Thujene 1 109 β-Anise aldehyde 1 110 β-Phellandrene 1 111 β-Pinene 1 112 γ-Cadinene 1 113 γ-terpinene 1 114 γ-Terpinene 1 115 Δ3-Carene 1 116 δ-Cadinene 1

See also

• heande:Composition of cinnamon dried bark essential oil
• heande:Composition of plant-based food supplements
• Compound intake estimator