Compound intake estimator: Difference between revisions

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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

Intake and risk estimates

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.

Compound concentrations

The following code allows to calculate the mean and median value of the concentration of each compound in the input data.

For seeing details of what the code actually does, click Show code. The code will appear, with documentation of each part on rows starting with ###. In brief, the code reads a piece of concentration and other data from Opasnet database, selects the rows defined by the user through the interface above, and shows the data on a summary table and a few graphs.

Rationale

Benefit-risk assessment

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)

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.

You can fill in the following information:

Name of plant food supplement (PFS) product. If this is given, it is shown on the graphs and outputs as title.
Fraction of essential oil in the supplement. How much (as a percentage) does the total product contain the essential oil?
Amount of supplement per day. How much is the supplement (the total product) consumed per day (given as grams of product per day)?
The compound concentration data is taken from a table on this page. However, if you know that the product contains something that is not listed in the table, you may add the information here.
- Name of additional compound. The name is used to combine the concentration data with toxicological data. So, the name should be found in one of these two tables on this page: Cramer classes of compounds, or Guidance values.
- Concentration of the additional compound in the supplement. This is given as mg of compound per g of PFS product.
Show intermediate results? If Yes is selected, you will see a table of intake estimates for each compound, and the same data as a graph. Otherwise, only a default output is shown (see below).
Which extraction techniques to consider? Different extraction methods result in different concentration estimates even from the same product. Some of the toxicological information is given having a particular extraction method in mind, and it is not always possible to use toxicological information with any concentration information. Therefore, if you know that some extraction methods should not be used to estimate toxicology, you can unselect them from the list.
The default outcome shows a graph where the level of concern is shown for each compound. However, because there are dozens of compounds in any product, the graph shows only those which has the level more than one, i.e. those compounds that need further scrutiny.

Concentrations

The concentration code allows to calculate the mean and median value of the concentration of each compound that appear in table in a new page. In addition, it is possible to plot the concentration (expressed as percentage of essential oil) of each compound reported in the table below. Each single value is reported in the chart.

Data

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

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

# 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", # amount of food supplement consumed
		"compound.conc", # concentration of compounds in essential oil
		"oil.amount", # amount of essential oil consumed in addition to the supplement product
		"fr.const", # fraction of essential oil in the supplement product
		"extraction", # Which extraction methods the user wants to consider
		"addcomp" # concentrations of compounds in the essential oil (missing from default data)
	)), 
	formula = function(...) {

		compound.conc <- compound.conc * 10 * extraction 
		# remove extractions that are not considered.
		# compound.conc from w/w% to mg/g

		compound.conc <- oapply(compound.conc, cols = "Extraction.technique", fun = mean)
		# removes index Extraction.technique, which is no longer needed.

		compound.conc@output <- orbind(compound.conc, addcomp * 1)
		# addcomp must not contain columns that are not in food.conc. It must contain Compound.
		# It is multiplied by 1 to make the result column name "Result".

#		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 * fr.const / 100 + oil.amount) * compound.conc / 60 
		# calculates the oil intake from food supplement and then adds direct oil intake.
		# scale to 60 kg person
		# fr.const from % to fraction

		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 (temporary)
# cramer.values are the thresholds of toxicological concern for each Cramer class (temporary)

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)
	}
)

extraction <- Ovariable("extraction", ddata = "Op_en6193.extraction_techniques")
extraction@data$extractionResult <- 1

### Define formatting for the concentration graphs that will be shown.

concplot <- function(condition) {
	dat <- compound.conc@output[condition, ]
	if(nrow(dat) != 0) {
		out <- ggplot(dat, 
			aes(
				x = Compound, 
				y = compound.concResult, 
				colour = Extraction.technique
			)) + 
			geom_point(size=5) +
			theme_grey(base_size = 24) +
			theme(axis.text.x = element_text(angle = 90, hjust = 1)) + 
			labs(y = "Concentration (%)")
		return(out)
	} else {
		 warning("No data to show")
	}
}

objects.store(compound.intake, guidance.values, food.risk, extraction, concplot)

cat("The following objects were successfully stored:
compound.intake, guidance.values, food.risk, extraction, concplot\n")

Using different data on different layers: [1].

Extraction techniques

Extraction techniques(-)
Obs	Extraction.technique	Notes
1	Not known
2	Hydrodistillation
3	Solvent extraction
4	Steam distillation
5	Supercritical fluid extraction, 120 bar, 40 °C
6	Supercritical fluid extraction, 90 bar, 40 °C
7	Supercritical fluid extraction, 90 bar, 50 °C
8	Superheated water extraction, 100 °C
9	Superheated water extraction, 150 °C
10	Superheated water extraction, 200 °C
11	Solid phase

Data about guidance values

^[1]

Guidance values(mg /kg-BW /d)
Obs	Compound	Guidance	Result	Notes
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	BMDL10	3.3-6.5	Data said BMD10 but probably BMDL10
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)^[2]

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

References

↑ New data obtained from Antonella Guzzon, personal communication (email) 31 March 2014.
↑ 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] New data obtained from Antonella Guzzon, personal communication (email) 31 March 2014.

[EFSA_2012-2] 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]

[2]

@@ Line 14: / Line 14: @@
 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.
-<rcode label='Intakes and risks' embed=1 graphics=1
+<rcode label='Intakes and risks' embed=0 graphics=1
 variables="
 	name:name|description:Name of plant food supplement|type:text|default:Cinnamon essential oil|
 category:Intake information|
+	name:food.amount|description:Amount of supplement per day (g /d)|type:text|default:1|
 	name:fr.const|description:Fraction of essential oil in the supplement (%)|type:text|default:10|
-	name:food.amount|description:Amount of supplement per day (g /d)|default:1|
+	name:oil.amount|description:Amount of essential oil consumed directly per day (g /d)|type:text|default:0|
 	name:intermediates|description:Show intermediate results?|
 		type:selection|options:FALSE;No;TRUE;Yes|default:FALSE|
@@ Line 58: / Line 59: @@
 objects.latest("Op_en6193", code_name="initiate")
+ls()
 # compound.conc is the concentration of compounds in the critical constituent.
 # Data comes from [[Compound intake estimator]].
@@ Line 75: / Line 76: @@
 fr.const <- Ovariable("fr.const", data = data.frame(Result = fr.const))
+# oil.amount is the amount that the consumer consumes essential oil in addition to the product (g /day)
+oil.amount <- Ovariable("oil.amount", data = data.frame(Result = oil.amount))
 # extraction is a list of extraction techniques to consider. (by default, all extraction techniques)
+oil.amount
+food.amount
 if(exists('extracted')) {
@@ Line 367: / Line 375: @@
 ################## 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
@@ Line 377: / Line 384: @@
 compound.intake <- Ovariable("compound.intake",
 	dependencies = data.frame(Names = c(
-		"food.amount",
+		"food.amount", # amount of food supplement consumed
-		"compound.conc",
+		"compound.conc", # concentration of compounds in essential oil
-		"fr.const",
+		"oil.amount", # amount of essential oil consumed in addition to the supplement product
-		"extraction",
+		"fr.const", # fraction of essential oil in the supplement product
-		"addcomp"
+		"extraction", # Which extraction methods the user wants to consider
+		"addcomp" # concentrations of compounds in the essential oil (missing from default data)
 	)),
 	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.
+		compound.conc <- compound.conc * 10 * extraction
+		# remove extractions that are not considered.
+		# compound.conc from w/w% to mg/g
+		compound.conc <- oapply(compound.conc, cols = "Extraction.technique", fun = mean)
+		# removes index Extraction.technique, which is no longer needed.
+		compound.conc@output <- orbind(compound.conc, addcomp * 1)
+		# addcomp must not contain columns that are not in food.conc. It must contain Compound.
+		# It is multiplied by 1 to make the result column name "Result".
-		# addcomp  must not contain columns that are not in food.conc. It is multiplied by 1 to make
+#		test <- colnames(food.conc@output) != "Extraction.technique"
-		# the result column name "Result".
+#		food.conc@marginal	<- food.conc@marginal[test]
-		food.conc@output <- orbind(food.conc, addcomp * 1)
+#		food.conc@output 	<- food.conc@output[test] # unit: mg /g
-		test <- colnames(food.conc@output) != "Extraction.technique"
+		out <- (food.amount * fr.const / 100 + oil.amount) * compound.conc / 60
-		food.conc@marginal	<- food.conc@marginal[test]
+		# calculates the oil intake from food supplement and then adds direct oil intake.
-		food.conc@output 	<- food.conc@output[test] # unit: mg /g
+		# scale to 60 kg person
+		# fr.const from % to fraction
-		out <- food.amount / 60 * food.conc # scale to 60 kg person
 		return(out)
 	}