httk (version 2.5.0)

predict_partitioning_schmitt: Predict partition coefficients using the method from Schmitt (2008).

Description

This function implements the method from Schmitt (2008) for predicting the tissue to unbound plasma partition coefficients for the tissues contained in the tissue.data table. The method has been modified by Pearce et al. (2017) based on an evaluation using in vivo measured partition coefficients.

To understand this method, it is important to recognize that in a given media the fraction unbound in that media is inverse of the media:water partition coefficient. In Schmitt's model, each tissue is composed of cells and interstitium, with each cell consisting of neutral lipid, neutral phospholipid, water, protein, and acidic phospholipid. Each tissue cell is defined as the sum of separate compartments for each constituent, all of which partition with a shared water compartment. The partitioning between the cell components and cell water is compound specific and determined by log Pow (in neutral lipid partitioning), membrane affinity (phospholipid and protein partitioning), and pKa (neutral lipid and acidic phospholipid partitioning). For a given compound the partitioning into each component is identical across tissues. Thus the differences among tissues are driven by their composition, that is, the varying volumes of components such as neutral lipid. However, pH differences across tissues also determine small differences in partitioning between cell and plasma water. The fup is used as the plasma water to total plasma partition coefficient and to approximate the partitioning between interstitial protein and water.

A regression is used to predict membrane affinity when measured values are not available (calc_ma). The regressions for correcting each tissue are performed on tissue plasma partition coefficients (Ktissue2pu * Funbound.plasma) calculated with the corrected Funbound.plasma value and divided by this value to get Ktissue2pu. Thus the regressions should be used with the corrected Funbound.plasma.

A separate regression is used when adjusted.Funbound.plasma is FALSE.

The red blood cell regression can be used but is not by default because of the span of the data used for evaluation, reducing confidence in the regression for higher and lower predicted values.

Human tissue volumes are used for species other than Rat.

Usage

predict_partitioning_schmitt(
  chem.name = NULL,
  chem.cas = NULL,
  dtxsid = NULL,
  species = "Human",
  model = "pbtk",
  default.to.human = FALSE,
  parameters = NULL,
  alpha = 0.001,
  adjusted.Funbound.plasma = TRUE,
  regression = TRUE,
  regression.list = c("brain", "adipose", "gut", "heart", "kidney", "liver", "lung",
    "muscle", "skin", "spleen", "bone"),
  tissues = NULL,
  minimum.Funbound.plasma = 1e-04,
  suppress.messages = FALSE
)

Value

Returns tissue to unbound plasma partition coefficients for each tissue.

Arguments

chem.name

Either the chemical name or the CAS number must be specified.

chem.cas

Either the chemical name or the CAS number must be specified.

dtxsid

EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard) the chemical must be identified by either CAS, name, or DTXSIDs

species

Species desired (either "Rat", "Rabbit", "Dog", "Mouse", or default "Human").

model

Model for which partition coefficients are neeeded (for example, "pbtk", "3compartment")

default.to.human

Substitutes missing animal values with human values if true (hepatic intrinsic clearance or fraction of unbound plasma).

parameters

Chemical parameters from parameterize_schmitt overrides chem.name, dtxsid, and chem.cas.

alpha

Ratio of Distribution coefficient D of totally charged species and that of the neutral form

adjusted.Funbound.plasma

Whether or not to use Funbound.plasma adjustment.

regression

Whether or not to use the regressions. Regressions are used by default.

regression.list

Tissues to use regressions on.

tissues

Vector of desired partition coefficients. Returns all by default.

minimum.Funbound.plasma

Monte Carlo draws less than this value are set equal to this value (default is 0.0001 -- half the lowest measured Fup in our dataset).

suppress.messages

Whether or not the output message is suppressed.

Author

Robert Pearce

References

Schmitt, Walter. "General approach for the calculation of tissue to plasma partition coefficients." Toxicology in Vitro 22.2 (2008): 457-467.

Birnbaum, L., et al. "Physiological parameter values for PBPK models." International Life Sciences Institute, Risk Science Institute, Washington, DC (1994).

Pearce, Robert G., et al. "Evaluation and calibration of high-throughput predictions of chemical distribution to tissues." Journal of pharmacokinetics and pharmacodynamics 44.6 (2017): 549-565.

Yun, Y. E., and A. N. Edginton. "Correlation-based prediction of tissue-to-plasma partition coefficients using readily available input parameters." Xenobiotica 43.10 (2013): 839-852.

See Also

parameterize_schmitt

tissue.data

calc_ma

Examples

library(httk)

# Predict the partition coefficients by chemical id:
PCs1 <- predict_partitioning_schmitt(chem.name='ibuprofen')

# Create a list of parameters (that you can potentially change):
p <- parameterize_schmitt(chem.name="ibuprofen")

# Predict the partition coefficients using a list of parameters:
PCs2 <- predict_partitioning_schmitt(parameters = p)

# Check that all the parameter values are the same:
all(unlist(PCs1)==unlist(PCs2))

# Predict partition coefficients without using Pearce et al. (2017) calibrations:
PCs3 <- predict_partitioning_schmitt(chem.name='ibuprofen',regression=FALSE)

# Lump the tissues into the compartments for model "pbtk":
lump_tissues(PCs1)

# Lump the tissues into a single volume of distribution
calc_vdist(parameters=c(p,PCs1))