httk (version 2.5.0)

tissue.data: Tissue composition and species-specific physiology parameters

Description

This data set contains values from Schmitt (2008) and Ruark et al. (2014) describing the composition of specific tissues and from Birnbaum et al. (1994) describing volumes of and blood flows to those tissues, allowing parameterization of toxicokinetic models for human, mouse, rat, dog, or rabbit. Tissue volumes were calculated by converting the fractional mass of each tissue with its density (both from ICRP), lumping the remaining tissues into the rest-of-body, excluding the mass of the gastrointestinal contents.

Usage

tissue.data

Format

A data.frame containing 406 rows and 5 columns.

Column	Description
Tissue	The tissue being described
Species	The species being described
Reference	The reference for the value reported
variable	The aspect of the tissue being characterized
value	The value for the variable for the given tissue and species

Author

John Wambaugh, Robert Pearce, and Nisha Sipes

Details

Many of the parameters were compiled initially in Table 2 of Schmitt (2009). The full list of tissue variables described is:

Variable	Description	Units
Fcell	Cellular fraction of total tissue volume	fraction
Fint	Interstitial fraction of total tissue volume	fraction
FWc	Fraction of cell volume that is water	fraction
FLc	Fraction of cell volume that is lipid	fraction
FPc	Fraction of cell volume that is protein	fraction
Fn_Lc	Fraction of cellular lipid tht is neutral lipid	fraction
Fn_PLc	Fraction of cellular lipid tht is neutral phospholipid	fraction
Fa_PLc	Fraction of cellular lipid tht is acidic phospholipid	fraction
pH	Negative logarithm of H+ ion concentration	unitless
Density	Tissue density	g/cm^3
Vol	Tissue volume	L/kg
Flow	Blood flow to tissue	mL/min/kg^(3/4)

New tissues can be added to this table to generate their partition coefficients.

References

birnbaum1994physiologicalhttk

ruark2014predictinghttk

schmitt2008generalhttk

snyder1974reporthttk

wambaugh2015toxicokinetichttk

See Also

predict_partitioning_schmitt

Examples

# We can add thyroid to the tissue data by making a row containing
# its data, subtracting the volumes and flows from the rest-of-body, 
# and binding the row to tissue.data. Here we assume it contains the same 
# partition coefficient data as the spleen and a tenth of the volume and  
# blood flow:
new.tissue <- subset(tissue.data,Tissue == "spleen")
new.tissue[, "Tissue"] <- "thyroid"
new.tissue[new.tissue$variable %in% c("Vol (L/kg)",
"Flow (mL/min/kg^(3/4))"),"value"] <- new.tissue[new.tissue$variable
%in% c("Vol (L/kg)","Flow (mL/min/kg^(3/4))"),"value"] / 10
tissue.data[tissue.data$Tissue == "rest", "value"] <-
tissue.data[tissue.data$Tissue == "rest", "value"] -
new.tissue[new.tissue$variable %in% c("Vol (L/kg)",
"Flow (mL/min/kg^(3/4))"),"value"]
tissue.data <- rbind(tissue.data, new.tissue)

# We can add a new species (for example, wolverines) by adding new information
# to the physiology.data and tissue.data tables. It can be convenient to start by
# by replicating the data from another species and adjusting as appropriate:

# Copy physiology data from rabbit:
new.species <- physiology.data[,"Rabbit"]
names(new.species) <- physiology.data[,"Parameter"]
rabbit.BW <- new.species["Average BW"] 
# Rausch and Pearson (1972) https://doi.org/10.2307/3799057 :
new.species["Average BW"] <- 31.2 
# Thiel et al. (2019) https://doi.org/10.1186/s12983-019-0319-8 :
new.species["Average Body Temperature"] <- 38.5 

# Add new physiology data column to physiology.data table"
physiology.data <- cbind(physiology.data, new.species)
colnames(physiology.data)[length(colnames(physiology.data))] <- "Wolverine"

# Copy tissue data from rabbit:
new.tissue.data <- subset(tissue.data,Species=="Rabbit")
new.tissue.data$Species <- "Wolverine"

# Add new tissue data rows to tissue.data table:
tissue.data <- rbind(tissue.data, new.tissue.data)

# Species is now available for calculations:
calc_mc_css(chem.cas="80-05-7",
            species="wolverine",
            parameterize.arg.list=list(default.to.human=TRUE),
            suppress.messages=TRUE,
            samples = 100)