Compound intake estimator: Difference between revisions

Revision as of 13:27, 30 October 2013

This page is a knowledge crystal of subtype method. The page identifier is Op_en6193
Moderator:Jouni (see all)

Upload data {{#opasnet_base_link:Op_en6193}}

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.

+ 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	Cinnamaldehyde	ADI	0.7
2	Safrole	BMDL10	1.9-5.1
3	α-Terpineol	ADI	1.2
4	4-Terpineol	ADI	1.2
5	Benzylbenzoate	ADI	5
6	Cinnamaldehyde	ADI	0.7
7	Eugenol	ADI	2.5
8	Linalool	ADI	0.5
9	Coumarin	TDI	0.1

Cramer values(µg /kg-BW /d)
Obs	Cramer class	Threshold of toxicological concern
1	1	30
2	2	1.5
3	3	1.5

Values 30 and 1.5 µg /kg /d come from EFSA (2012)^[1]

Cramer classes of compounds

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

References

↑ EFSA (2012). Scientific Opinion on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA Journal 2012;10(7):2750

[EFSA_2012-1] EFSA (2012). Scientific Opinion on Exploring options for providing advice about possible human health risks based on the concept of Threshold of Toxicological Concern (TTC). EFSA Journal 2012;10(7):2750

[1]