Nothing
#This file is used by roxygen2 to generate man files (documentation) for data
#sets included in the package.
#'Reference tissue masses and flows from tables in McNally et al. 2014.
#'
#'Reference tissue masses, flows, and residual variance distributions from
#'Tables 1, 4, and 5 of McNally et al. 2014.
#'
#'@format A data.table with variables: \describe{\item{\code{tissue}}{Body
#' tissue} \item{\code{gender}}{Gender: Male or Female}
#' \item{\code{mass_ref}}{Reference mass in kg, from Reference Man}
#' \item{\code{mass_cv}}{Coefficient of variation for mass}
#' \item{\code{mass_dist}}{Distribution for mass: Normal or Log-normal}
#' \item{\code{flow_ref}}{Reference flow in L/h, from Reference Man}
#' \item{\code{flow_cv}}{Coefficient of variation for flow (all normally
#' distributed)} \item{\code{height_ref}}{Reference heights (by gender)}
#' \item{\code{CO_ref}}{Reference cardiac output by gender}
#' \item{\code{flow_frac}}{Fraction of CO flowing to each tissue:
#' \code{flow_ref}/\code{CO_ref}}}
#'@source McNally K, Cotton R, Hogg A, Loizou G. "PopGen: A virtual human
#' population generator." Toxicology 315, 70-85, 2004.
#'@keywords data
#'@keywords httk-pop
#'
#'@author Caroline Ring
#'
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' environmental chemicals by simulating toxicokinetic variability."
#' Environment International 106 (2017): 105-118
"mcnally_dt"
#' A timestamp of table creation
#'
#' The Tables.RData file is separately created as part of building a new
#' release of HTTK. This time stamp indicates the script used to build the file
#' and when it was run.
#'
#' @author John Wambaugh
"Tables.Rdata.stamp"
#' Reference for EPA Physico-Chemical Data
#'
#' The physico-chemical data in the chem.phys_and_invitro.data table are
#' obtained from EPA's Comptox Chemicals dashboard. This variable indicates
#' the date the Dashboard was accessed.
#' @source \url{https://comptox.epa.gov/dashboard}
#'
#' @author John Wambaugh
"EPA.ref"
#'Pre-processed NHANES data.
#'
#'NHANES data on demographics, anthropometrics, and some laboratory measures,
#'cleaned and combined into a single data set.
#'
#'@format A data.table with 23620 rows and 12
#' variables. \describe{ \item{seqn}{NHANES unique identifier for individual
#' respondents.} \item{sddsrvyr}{NHANES two-year cycle: one of "NHANES
#' 2013-2014", "NHANES 2015-2016", "NHANES 2017-2018".} \item{riagendr}{Gender:
#' "Male" or "Female"} \item{ridreth1}{Race/ethnicity category: one of "Mexican
#' American", "Non-Hispanic White", "Non-Hispanic Black", "Other", "Other
#' Hispanic".} \item{ridexagm}{Age in months at the time of examination (if not
#' recorded by NHANES, it was imputed based on age at the time of screening)}
#' \item{ridexagy}{Age in years at the time of examination (if not recorded by
#' NHANES, it was imputed based on age at the time of screening)}
#' \item{bmxwt}{Weight in kg} \item{lbxscr}{Serum creatinine, mg/dL}
#' \item{lbxhct}{Hematocrit, percent by volume of blood composed of red blood
#' cells} \item{\code{wtmec6yr}}{6-year sample weights for combining 3 cycles,
#' computed by dividing 2-year sample weights by 3.}
#' \item{\code{bmxhtlenavg}}{Average of height and recumbent length if both
#' were measured; if only one was measured, takes value of the one that was
#' measured.} \item{\code{weight_class}}{One of Underweight, Normal,
#' Overweight, or Obese. Assigned using methods in
#' \code{\link{get_weight_class}}.} }
#'
#'@source \url{https://wwwn.cdc.gov/nchs/nhanes/Default.aspx}
#'
#'@keywords data
#'@keywords httk-pop
#'
#'@author Caroline Ring
#'
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' environmental chemicals by simulating toxicokinetic variability."
#' Environment International 106 (2017): 105-118
"mecdt"
#'CDC BMI-for-age charts
#'
#'Charts giving the BMI-for-age percentiles for boys and girls ages 2-18
#'
#'For children ages 2 to 18, weight class depends on the BMI-for-age percentile.
#'\describe{
#'\item{Underweight}{<5th percentile}
#'\item{Normal weight}{5th-85th percentile}
#'\item{Overweight}{85th-95th percentile}
#'\item{Obese}{>=95th percentile}
#'}
#'
#' @format A data.table with 434 rows and 5 variables: \describe{
#' \item{Sex}{Female or Male} \item{Agemos}{Age in months} \item{P5}{The 5th
#' percentile BMI for the corresponding sex and age} \item{P85}{The 85th
#' percentile BMI for the corresponding sex and age} \item{P95}{The 95th
#' percentile BMI for the corresponding sex and age} }
#' @source \url{https://www.cdc.gov/growthcharts/data/zscore/bmiagerev.csv}
#'
#'@keywords data
#'@keywords httk-pop
#'
#'@author Caroline Ring
#'
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#'environmental chemicals by simulating toxicokinetic variability." Environment
#'International 106 (2017): 105-118
"bmiage"
#'WHO weight-for-length charts
#'
#'Charts giving weight-for-length percentiles for boys and girls under age 2.
#'
#'For infants under age 2, weight class depends on weight for length percentile.
#'#'\describe{ \item{Underweight}{<2.3rd percentile} \item{Normal
#'weight}{2.3rd-97.7th percentile} \item{Obese}{>=97.7th percentile} }
#'
#'@format a data.table with 262 rows and 4 variables:
#'
#' \describe{ \item{Sex}{"Male" or "Female"} \item{Length}{Recumbent length in
#' cm} \item{P2.3}{The 2.3rd percentile weight in kg for the corresponding sex
#' and recumbent length} \item{P97.7}{The 97.7th percentile weight in kg for
#' the corresponding sex and recumbent length}}
#'
#'@source
#'\url{https://www.cdc.gov/growthcharts/who/boys_weight_head_circumference.htm}
#'and
#'\url{https://www.cdc.gov/growthcharts/who/girls_weight_head_circumference.htm}
"wfl"
#'@keywords data
#'
#'@author Caroline Ring
#'@keywords httk-pop
#'
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#'environmental chemicals by simulating toxicokinetic variability." Environment
#'International 106 (2017): 105-118
"wfl"
#'KDE bandwidth for residual variability in height/weight
#'
#'Bandwidths used for a two-dimensional kernel density estimation of the joint
#'distribution of residual errors around smoothing spline fits of height vs. age
#'and weight vs. age for NHANES respondents in each of ten combinations of sex
#'and race/ethnicity categories.
#'
#'Each matrix is a variance-covariance matrix for a two-dimensional normal
#'distribution: this is the bandwidth to be used for a two-dimensional kernel
#'density estimation (KDE) (using a two-dimensional normal kernel) of the joint
#'distribution of residual errors around smoothing spline fits of height vs. age
#'and weight vs. age for NHANES respondents in the specified sex and
#'race/ethnicity category. Optimal bandwidths were pre-calculated by doing the
#'smoothing spline fits, getting the residuals, then calling
#'\code{\link[ks]{kde}} on the residuals (which calls \code{\link[ks]{Hpi}} to
#'compute the plug-in bandwidth).
#'
#'Used by HTTK-Pop only in "virtual individuals" mode (i.e.
#'\code{\link{httkpop_generate}} with \code{method = "v"}), in
#'\code{\link{gen_height_weight}}.
#'
#'@format A named list with 10 elements, each a matrix with 2 rows and 2
#' columns. Each list element corresponds to, and is named for, one combination
#' of NHANES sex categories (Male and Female) and NHANES race/ethnicity
#' categories (Mexican American, Other Hispanic, Non-Hispanic White,
#' Non-Hispanic Black, and Other).
#'
#'@keywords data
#'
#'@author Caroline Ring
#'@keywords httk-pop
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' environmental chemicals by simulating toxicokinetic variability."
#' Environment International 106 (2017): 105-118
"hw_H"
#'KDE bandwidths for residual variability in hematocrit
#'
#'Bandwidths used for a one-dimensional kernel density estimation of the
#'distribution of residual errors around smoothing spline fits of hematocrit vs.
#'age for NHANES respondents in each of ten combinations of sex and
#'race/ethnicity categories.
#'
#'Each matrix is the standard deviation for a normal distribution: this is the
#'bandwidth to be used for a kernel density estimation (KDE) (using a normal
#'kernel) of the distribution of residual errors around smoothing spline fits of
#'hematocrit vs. age for NHANES respondents in the specified sex and
#'race/ethnicity category. Optimal bandwidths were pre-calculated by doing the
#'smoothing spline fits, getting the residuals, then calling
#'\code{\link[ks]{kde}} on the residuals (which calls \code{\link[ks]{hpi}} to
#'compute the plug-in bandwidth).
#'
#'Used by HTTK-Pop only in "virtual individuals" mode (i.e.
#'\code{\link{httkpop_generate}} with \code{method = "v"}), in
#'\code{\link{estimate_hematocrit}}.
#'
#'@format A named list with 10 elements, each a numeric value. Each list element
#' corresponds to, and is named for, one combination of NHANES sex categories
#' (Male and Female) and NHANES race/ethnicity categories (Mexican American,
#' Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other).
#'
#'@keywords data
#'
#'@author Caroline Ring
#'@keywords httk-pop
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' environmental chemicals by simulating toxicokinetic variability."
#' Environment International 106 (2017): 105-118
"hct_h"
#'KDE bandwidths for residual variability in serum creatinine
#'
#'Bandwidths used for a one-dimensional kernel density estimation of the
#'distribution of residual errors around smoothing spline fits of serum
#'creatinine vs. age for NHANES respondents in each of ten combinations of sex
#'and race/ethnicity categories.
#'
#'Each matrix is the standard deviation for a normal distribution: this is the
#'bandwidth to be used for a kernel density estimation (KDE) (using a normal
#'kernel) of the distribution of residual errors around smoothing spline fits of
#'serum creatinine vs. age for NHANES respondents in the specified sex and
#'race/ethnicity category. Optimal bandwidths were pre-calculated by doing the
#'smoothing spline fits, getting the residuals, then calling
#'\code{\link[ks]{kde}} on the residuals (which calls \code{\link[ks]{hpi}} to
#'compute the plug-in bandwidth).
#'
#'Used by HTTK-Pop only in "virtual individuals" mode (i.e.
#'\code{\link{httkpop_generate}} with \code{method = "v"}), in
#'\code{\link{gen_serum_creatinine}}.
#'
#'@format A named list with 10 elements, each a numeric value. Each list element
#' corresponds to, and is named for, one combination of NHANES sex categories
#' (Male and Female) and NHANES race/ethnicity categories (Mexican American,
#' Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other).
#'
#'@keywords data
#'
#'@author Caroline Ring
#'@keywords httk-pop
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' environmental chemicals by simulating toxicokinetic variability."
#' Environment International 106 (2017): 105-118
"scr_h"
#' Microtiter Plate Well Descriptions for Armitage et al. (2014) Model
#'
#' Microtiter Plate Well Descriptions for Armitage et al. (2014) model from
#' Honda et al. (2019)
#'
#' @format A data frame / data table with 11 rows and 8 variables:
#' \describe{
#' \item{sysID}{Identifier for each multi-well plate system}
#' \item{well_desc}{Well description}
#' \item{well_number}{Number of wells on plate}
#' \item{area_bottom}{Area of well bottom in mm^2}
#' \item{cell_yield}{Number of cells}
#' \item{diam}{Diameter of well in mm}
#' \item{v_total}{Total volume of well in uL)}
#' \item{v_working}{Working volume of well in uL}
#' }
#'
#' @source \url{https://www.corning.com/catalog/cls/documents/application-notes/CLS-AN-209.pdf}
#'
#' @keywords data
#'
#' @author Greg Honda
#'
#' @references Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol.
#'2014, 48, 9770-9779. dx.doi.org/10.1021/es501955g
#' @references Honda, Gregory S., et al. "Using the Concordance of In Vitro and
#'In Vivo Data to Evaluate Extrapolation Assumptions", PloS ONE 14.5 (2019): e0217564.
"well_param"
#' Armitage et al. (2014) Model Inputs from Honda et al. (2019)
#'
#' @format A data frame with 53940 rows and 10 variables:
#' \describe{
#' \item{MP}{}
#' \item{MW}{}
#' \item{casrn}{}
#' \item{compound_name}{}
#' \item{gkaw}{}
#' \item{gkow}{}
#' \item{gswat}{}
#' }
#' @source \url{https://www.diamondse.info/}
#'
#'@keywords data
#'
#'@author Greg Honda
#'
#'@references Armitage, J. M.; Wania, F.; Arnot, J. A. Environ. Sci. Technol.
#'2014, 48, 9770-9779. dx.doi.org/10.1021/es501955g
#'@references Honda, Gregory S., et al. "Using the Concordance of In Vitro and
#'In Vivo Data to Evaluate Extrapolation Assumptions", PloS ONE 14.5 (2019): e0217564.
"armitage_input"
#' DRUGS|NORMAN: Pharmaceutical List with EU, Swiss, US Consumption Data
#'
#' SWISSPHARMA is a list of pharmaceuticals with consumption data from
#' Switzerland, France, Germany and the USA, used for a suspect
#' screening/exposure modelling approach described in
#' Singer et al 2016, DOI: 10.1021/acs.est.5b03332. The original data is
#' available on the NORMAN Suspect List Exchange.
#'
#'@source \url{https://comptox.epa.gov/dashboard/chemical_lists/swisspharma}
#'@keywords data
#'
#'@references Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and
#'Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.
"pharma"
#' in vitro Toxicokinetic Data from Wambaugh et al. (2019)
#'
#' These data are the new HTTK in vitro data for chemicals reported in Wambaugh
#' et al. (2019) They
#' are the processed values used to make the figures in that manuscript.
#' These data summarize the results of Bayesian analysis of the in vitro
#' toxicokinetic experiments conducted by Cyprotex to characterize fraction
#' unbound in the presence of pooled human plasma protein and the intrnsic
#' hepatic clearance of the chemical by pooled human hepatocytes.
#'
#' @format A data frame with 496 rows and 17 variables:
#' \describe{
#' \item{Compound}{The name of the chemical}
#' \item{CAS}{The Chemical Abstracts Service Registry Number}
#' \item{Human.Clint}{Median of Bayesian credible interval for intrinsic
#' hepatic clearance (uL/min/million hepatocytes)]}
#' \item{Human.Clint.pValue}{Probability that there is no clearance}
#' \item{Human.Funbound.plasma}{Median of Bayesian credibl interval for
#' fraction of chemical free in the presence of plasma}
#' \item{pKa_Accept}{pH(s) at which hydrogen acceptor sites (if any) are at
#' equilibrium}
#' \item{pKa_Donor}{pH(s) at which hydrogne donor sites (if any) are at
#' equilibrium}
#' \item{DSSTox_Substance_Id}{Identifier for CompTox Chemical Dashboard}
#' \item{SMILES}{Simplified Molecular-Input Line-Entry System structure
#' description}
#' \item{Human.Clint.Low95}{Lower 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance (uL/min/million hepatocytes)}
#' \item{Human.Clint.High95}{Uppper 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance (uL/min/million hepatocytes)}
#' \item{Human.Clint.Point}{Point estimate of intrinsic hepatic clearance
#' (uL/min/million hepatocytes)}
#' \item{Human.Funbound.plasma.Low95}{Lower 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma}
#' \item{Human.Funbound.plasma.High95}{Upper 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma}
#' \item{Human.Funbound.plasma.Point}{Point estimate of the fraction of
#' chemical free in the presence of plasma}
#' \item{MW}{Molecular weight (Daltons)}
#' \item{logP}{log base ten of octanol:water partiion coefficient}
#' }
#' @source Wambaugh et al. (2019)
#'
#'@keywords data
#'
#'@author John Wambaugh
#'
#'@references Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and
#'Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.
"wambaugh2019"
#' Published toxicokinetic time course measurements
#'
#' This data set includes time and dose specific measurements of chemical
#' concentration in tissues taken from animals administered control doses of
#' the chemicals either orally or intravenously. This plasma concentration-time
#' data is from rat experiments reported in public sources. Toxicokinetic data
#' were retrieved from those studies by the Netherlands Organisation for
#' Applied Scientific Research (TNO) using curve stripping (TechDig v2). This
#' data is provided for statistical analysis as in Wambaugh et al. 2018.
#'
#'
#' @docType data
#' @format A data.frame containing 597 rows and 13 columns.
#' @author Sieto Bosgra
#' @references Aanderud L, Bakke OM (1983). Pharmacokinetics of antipyrine,
#' paracetamol, and morphine in rat at 71 ATA. Undersea Biomed Res.
#' 10(3):193-201. PMID: 6636344
#'
#' Aasmoe L, Mathiesen M, Sager G (1999). Elimination of methoxyacetic acid and
#' ethoxyacetic acid in rat. Xenobiotica. 29(4):417-24. PMID: 10375010
#'
#' Ako RA. Pharmacokinetics/pharmacodynamics (PK/PD) of oral diethylstilbestrol
#' (DES) in recurrent prostate cancer patients and of oral dissolving film
#' (ODF)-DES in rats. PhD dissertation, College of Pharmacy, University of
#' Houston, USA, 2011.
#'
#' Anadon A, Martinez-Larranaga MR, Fernandez-Cruz ML, Diaz MJ, Fernandez MC,
#' Martinez MA (1996). Toxicokinetics of deltamethrin and its 4'-HO-metabolite
#' in the rat. Toxicol Appl Pharmacol. 141(1):8-16. PMID: 8917670
#'
#' Binkerd PE, Rowland JM, Nau H, Hendrickx AG (1988). Evaluation of valproic
#' acid (VPA) developmental toxicity and pharmacokinetics in Sprague-Dawley
#' rats. Fundam Appl Toxicol. 11(3):485-93. PMID: 3146521
#'
#' Boralli VB, Coelho EB, Cerqueira PM, Lanchote VL (2005). Stereoselective
#' analysis of metoprolol and its metabolites in rat plasma with application to
#' oxidative metabolism. J Chromatogr B Analyt Technol Biomed Life Sci.
#' 823(2):195-202. PMID: 16029965
#'
#' Chan MP, Morisawa S, Nakayama A, Kawamoto Y, Sugimoto M, Yoneda M (2005).
#' Toxicokinetics of 14C-endosulfan in male Sprague-Dawley rats following oral
#' administration of single or repeated doses. Environ Toxicol. 20(5):533-41.
#' PMID: 16161119
#'
#' Cruz L, Castaneda-Hernandez G, Flores-Murrieta FJ, Garcia-Lopez P,
#' Guizar-Sahagun G (2002). Alteration of phenacetin pharmacokinetics after
#' experimental spinal cord injury. Proc West Pharmacol Soc. 45:4-5. PMID:
#' 12434508
#'
#' Della Paschoa OE, Mandema JW, Voskuyl RA, Danhof M (1998).
#' Pharmacokinetic-pharmacodynamic modeling of the anticonvulsant and
#' electroencephalogram effects of phenytoin in rats. J Pharmacol Exp Ther.
#' 284(2):460-6. PMID: 9454785
#'
#' Du B, Li X, Yu Q, A Y, Chen C (2010). Pharmacokinetic comparison of orally
#' disintegrating, beta-cyclodextrin inclusion complex and conventional tablets
#' of nicardipine in rats. Life Sci J. 7(2):80-4.
#'
#' Farris FF, Dedrick RL, Allen PV, Smith JC (1993). Physiological model for
#' the pharmacokinetics of methyl mercury in the growing rat. Toxicol Appl
#' Pharmacol. 119(1):74-90. PMID: 8470126
#'
#' Hays SM, Elswick BA, Blumenthal GM, Welsch F, Conolly RB, Gargas ML (2000).
#' Development of a physiologically based pharmacokinetic model of
#' 2-methoxyethanol and 2-methoxyacetic acid disposition in pregnant rats.
#' Toxicol Appl Pharmacol. 163(1):67-74. PMID: 10662606
#'
#' Igari Y, Sugiyama Y, Awazu S, Hanano M (1982). Comparative physiologically
#' based pharmacokinetics of hexobarbital, phenobarbital and thiopental in the
#' rat. J Pharmacokinet Biopharm. 10(1):53-75. PMID: 7069578
#'
#' Ito K, Houston JB (2004). Comparison of the use of liver models for
#' predicting drug clearance using in vitro kinetic data from hepatic
#' microsomes and isolated hepatocytes. Pharm Res. 21(5):785-92. PMID: 15180335
#'
#' Jia L, Wong H, Wang Y, Garza M, Weitman SD (2003). Carbendazim: disposition,
#' cellular permeability, metabolite identification, and pharmacokinetic
#' comparison with its nanoparticle. J Pharm Sci. 92(1):161-72. PMID: 12486692
#'
#' Kawai R, Mathew D, Tanaka C, Rowland M (1998). Physiologically based
#' pharmacokinetics of cyclosporine A: extension to tissue distribution
#' kinetics in rats and scale-up to human. J Pharmacol Exp Ther. 287(2):457-68.
#' PMID: 9808668
#'
#' Kim YC, Kang HE, Lee MG (2008). Pharmacokinetics of phenytoin and its
#' metabolite, 4'-HPPH, after intravenous and oral administration of phenytoin
#' to diabetic rats induced by alloxan or streptozotocin. Biopharm Drug Dispos.
#' 29(1):51-61. PMID: 18022993
#'
#' Kobayashi S, Takai K, Iga T, Hanano M (1991). Pharmacokinetic analysis of
#' the disposition of valproate in pregnant rats. Drug Metab Dispos.
#' 19(5):972-6. PMID: 1686245
#'
#' Kotegawa T, Laurijssens BE, Von Moltke LL, Cotreau MM, Perloff MD,
#' Venkatakrishnan K, Warrington JS, Granda BW, Harmatz JS, Greenblatt DJ
#' (2002). In vitro, pharmacokinetic, and pharmacodynamic interactions of
#' ketoconazole and midazolam in the rat. J Pharmacol Exp Ther. 302(3):1228-37.
#' PMID: 12183684
#'
#' Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K,
#' Baquie M, Waldmann T, Ensenat-Waser R, Jagtap S, Evans RM, Julien S,
#' Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou I, Heke M,
#' Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout
#' J, Verwei M, Vilo J, Kortenkamp A, Hescheler J, Hothorn L, Bremer S, van
#' Thriel C, Krause KH, Hengstler JG, Rahnenfuhrer J, Leist M, Sachinidis A
#' (2013). Human embryonic stem cell-derived test systems for developmental
#' neurotoxicity: a transcriptomics approach. Arch Toxicol. 87(1):123-43. PMID:
#' 23179753
#'
#' Leon-Reyes MR, Castaneda-Hernandez G, Ortiz MI (2009). Pharmacokinetic of
#' diclofenac in the presence and absence of glibenclamide in the rat. J Pharm
#' Pharm Sci. 12(3):280-7. PMID: 20067705
#'
#' Nagata M, Hidaka M, Sekiya H, Kawano Y, Yamasaki K, Okumura M, Arimori K
#' (2007). Effects of pomegranate juice on human cytochrome P450 2C9 and
#' tolbutamide pharmacokinetics in rats. Drug Metab Dispos. 35(2):302-5. PMID:
#' 17132763
#'
#' Okiyama M, Ueno K, Ohmori S, Igarashi T, Kitagawa H (1988). Drug
#' interactions between imipramine and benzodiazepines in rats. J Pharm Sci.
#' 77(1):56-63. PMID: 2894451
#'
#' Pelissier-Alicot AL, Schreiber-Deturmeny E, Simon N, Gantenbein M,
#' Bruguerolle B (2002). Time-of-day dependent pharmacodynamic and
#' pharmacokinetic profiles of caffeine in rats. Naunyn Schmiedebergs Arch
#' Pharmacol. 365(4):318-25. PMID: 11919657
#'
#' Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van
#' der Linden SC, Man H, Roelofs MJ, Schulpen SH, Schwarz M, Uibel F, van
#' Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B (2013).
#' Evaluation of an alternative in vitro test battery for detecting
#' reproductive toxicants. Reprod Toxicol. 38:53-64. PMID: 23511061
#'
#' Pollack GM, Li RC, Ermer JC, Shen DD (1985). Effects of route of
#' administration and repetitive dosing on the disposition kinetics of
#' di(2-ethylhexyl) phthalate and its mono-de-esterified metabolite in rats.
#' Toxicol Appl Pharmacol. Jun 30;79(2):246-56. PMID: 4002226
#'
#' Saadeddin A, Torres-Molina F, Carcel-Trullols J, Araico A, Peris JE (2004).
#' Pharmacokinetics of the time-dependent elimination of all-trans-retinoic
#' acid in rats. AAPS J. 6(1):1-9. PMID: 18465253
#'
#' Satterwhite JH, Boudinot FD (1991). Effects of age and dose on the
#' pharmacokinetics of ibuprofen in the rat. Drug Metab Dispos. 19(1):61-7.
#' PMID: 1673423
#'
#' Szymura-Oleksiak J, Panas M, Chrusciel W (1983). Pharmacokinetics of
#' imipramine after single and multiple intravenous administration in rats. Pol
#' J Pharmacol Pharm. 35(2):151-7. PMID: 6622297
#'
#' Tanaka C, Kawai R, Rowland M (2000). Dose-dependent pharmacokinetics of
#' cyclosporin A in rats: events in tissues. Drug Metab Dispos. 28(5):582-9.
#' PMID: 10772639
#'
#' Timchalk C, Nolan RJ, Mendrala AL, Dittenber DA, Brzak KA, Mattsson JL
#' (2002). A Physiologically based pharmacokinetic and pharmacodynamic
#' (PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and
#' humans. Toxicol Sci. Mar;66(1):34-53. PMID: 11861971
#'
#' Tokuma Y, Sekiguchi M, Niwa T, Noguchi H (1988). Pharmacokinetics of
#' nilvadipine, a new dihydropyridine calcium antagonist, in mice, rats,
#' rabbits and dogs. Xenobiotica 18(1):21-8. PMID: 3354229
#'
#' Treiber A, Schneiter R, Delahaye S, Clozel M (2004). Inhibition of organic
#' anion transporting polypeptide-mediated hepatic uptake is the major
#' determinant in the pharmacokinetic interaction between bosentan and
#' cyclosporin A in the rat. J Pharmacol Exp Ther. 308(3):1121-9. PMID:
#' 14617681
#'
#' Tsui BC, Feng JD, Buckley SJ, Yeung PK (1994). Pharmacokinetics and
#' metabolism of diltiazem in rats following a single intra-arterial or single
#' oral dose. Eur J Drug Metab Pharmacokinet. 19(4):369-73. PMID: 7737239
#'
#' Wambaugh, John F., et al. "Toxicokinetic triage for environmental
#' chemicals." Toxicological Sciences (2015): 228-237.
#'
#' Wang Y, Roy A, Sun L, Lau CE (1999). A double-peak phenomenon in the
#' pharmacokinetics of alprazolam after oral administration. Drug Metab Dispos.
#' 27(8):855-9. PMID: 10421610
#'
#' Wang X, Lee WY, Or PM, Yeung JH (2010). Pharmacokinetic interaction studies
#' of tanshinones with tolbutamide, a model CYP2C11 probe substrate, using
#' liver microsomes, primary hepatocytes and in vivo in the rat. Phytomedicine.
#' 17(3-4):203-11. PMID: 19679455
#'
#' Yang SH, Lee MG (2008). Dose-independent pharmacokinetics of ondansetron in
#' rats: contribution of hepatic and intestinal first-pass effects to low
#' bioavailability. Biopharm Drug Dispos. 29(7):414-26. PMID: 18697186
#'
#' Yeung PK, Alcos A, Tang J (2009). Pharmacokinetics and Hemodynamic Effects
#' of Diltiazem in Rats Following Single vs Multiple Doses In Vivo. Open Drug
#' Metab J. 3:56-62.
#' @source Wambaugh et al. 2018 Toxicological Sciences, in press
#' @keywords data
"chem.invivo.PK.data"
#' Summary of published toxicokinetic time course experiments
#'
#' This data set summarizes the time course data in the chem.invivo.PK.data
#' table. Maximum concentration (Cmax), time integrated plasma concentration
#' for the duration of treatment (AUC.treatment) and extrapolated to zero
#' concentration (AUC.infinity) as well as half-life are calculated. Summary
#' values are given for each study and dosage. These data can be used to
#' evaluate toxicokinetic model predictions.
#'
#'
#' @docType data
#' @format A data.frame containing 100 rows and 25 columns.
#' @author John Wambaugh
#' @references Aanderud L, Bakke OM (1983). Pharmacokinetics of antipyrine,
#' paracetamol, and morphine in rat at 71 ATA. Undersea Biomed Res.
#' 10(3):193-201. PMID: 6636344
#'
#' Aasmoe L, Mathiesen M, Sager G (1999). Elimination of methoxyacetic acid and
#' ethoxyacetic acid in rat. Xenobiotica. 29(4):417-24. PMID: 10375010
#'
#' Ako RA. Pharmacokinetics/pharmacodynamics (PK/PD) of oral diethylstilbestrol
#' (DES) in recurrent prostate cancer patients and of oral dissolving film
#' (ODF)-DES in rats. PhD dissertation, College of Pharmacy, University of
#' Houston, USA, 2011.
#'
#' Anadon A, Martinez-Larranaga MR, Fernandez-Cruz ML, Diaz MJ, Fernandez MC,
#' Martinez MA (1996). Toxicokinetics of deltamethrin and its 4'-HO-metabolite
#' in the rat. Toxicol Appl Pharmacol. 141(1):8-16. PMID: 8917670
#'
#' Binkerd PE, Rowland JM, Nau H, Hendrickx AG (1988). Evaluation of valproic
#' acid (VPA) developmental toxicity and pharmacokinetics in Sprague-Dawley
#' rats. Fundam Appl Toxicol. 11(3):485-93. PMID: 3146521
#'
#' Boralli VB, Coelho EB, Cerqueira PM, Lanchote VL (2005). Stereoselective
#' analysis of metoprolol and its metabolites in rat plasma with application to
#' oxidative metabolism. J Chromatogr B Analyt Technol Biomed Life Sci.
#' 823(2):195-202. PMID: 16029965
#'
#' Chan MP, Morisawa S, Nakayama A, Kawamoto Y, Sugimoto M, Yoneda M (2005).
#' Toxicokinetics of 14C-endosulfan in male Sprague-Dawley rats following oral
#' administration of single or repeated doses. Environ Toxicol. 20(5):533-41.
#' PMID: 16161119
#'
#' Cruz L, Castaneda-Hernandez G, Flores-Murrieta FJ, Garcia-Lopez P,
#' Guizar-Sahagun G (2002). Alteration of phenacetin pharmacokinetics after
#' experimental spinal cord injury. Proc West Pharmacol Soc. 45:4-5. PMID:
#' 12434508
#'
#' Della Paschoa OE, Mandema JW, Voskuyl RA, Danhof M (1998).
#' Pharmacokinetic-pharmacodynamic modeling of the anticonvulsant and
#' electroencephalogram effects of phenytoin in rats. J Pharmacol Exp Ther.
#' 284(2):460-6. PMID: 9454785
#'
#' Du B, Li X, Yu Q, A Y, Chen C (2010). Pharmacokinetic comparison of orally
#' disintegrating, beta-cyclodextrin inclusion complex and conventional tablets
#' of nicardipine in rats. Life Sci J. 7(2):80-4.
#'
#' Farris FF, Dedrick RL, Allen PV, Smith JC (1993). Physiological model for
#' the pharmacokinetics of methyl mercury in the growing rat. Toxicol Appl
#' Pharmacol. 119(1):74-90. PMID: 8470126
#'
#' Hays SM, Elswick BA, Blumenthal GM, Welsch F, Conolly RB, Gargas ML (2000).
#' Development of a physiologically based pharmacokinetic model of
#' 2-methoxyethanol and 2-methoxyacetic acid disposition in pregnant rats.
#' Toxicol Appl Pharmacol. 163(1):67-74. PMID: 10662606
#'
#' Igari Y, Sugiyama Y, Awazu S, Hanano M (1982). Comparative physiologically
#' based pharmacokinetics of hexobarbital, phenobarbital and thiopental in the
#' rat. J Pharmacokinet Biopharm. 10(1):53-75. PMID: 7069578
#'
#' Ito K, Houston JB (2004). Comparison of the use of liver models for
#' predicting drug clearance using in vitro kinetic data from hepatic
#' microsomes and isolated hepatocytes. Pharm Res. 21(5):785-92. PMID: 15180335
#'
#' Jia L, Wong H, Wang Y, Garza M, Weitman SD (2003). Carbendazim: disposition,
#' cellular permeability, metabolite identification, and pharmacokinetic
#' comparison with its nanoparticle. J Pharm Sci. 92(1):161-72. PMID: 12486692
#'
#' Kawai R, Mathew D, Tanaka C, Rowland M (1998). Physiologically based
#' pharmacokinetics of cyclosporine A: extension to tissue distribution
#' kinetics in rats and scale-up to human. J Pharmacol Exp Ther. 287(2):457-68.
#' PMID: 9808668
#'
#' Kim YC, Kang HE, Lee MG (2008). Pharmacokinetics of phenytoin and its
#' metabolite, 4'-HPPH, after intravenous and oral administration of phenytoin
#' to diabetic rats induced by alloxan or streptozotocin. Biopharm Drug Dispos.
#' 29(1):51-61. PMID: 18022993
#'
#' Kobayashi S, Takai K, Iga T, Hanano M (1991). Pharmacokinetic analysis of
#' the disposition of valproate in pregnant rats. Drug Metab Dispos.
#' 19(5):972-6. PMID: 1686245
#'
#' Kotegawa T, Laurijssens BE, Von Moltke LL, Cotreau MM, Perloff MD,
#' Venkatakrishnan K, Warrington JS, Granda BW, Harmatz JS, Greenblatt DJ
#' (2002). In vitro, pharmacokinetic, and pharmacodynamic interactions of
#' ketoconazole and midazolam in the rat. J Pharmacol Exp Ther. 302(3):1228-37.
#' PMID: 12183684
#'
#' Krug AK, Kolde R, Gaspar JA, Rempel E, Balmer NV, Meganathan K, Vojnits K,
#' Baquie M, Waldmann T, Ensenat-Waser R, Jagtap S, Evans RM, Julien S,
#' Peterson H, Zagoura D, Kadereit S, Gerhard D, Sotiriadou I, Heke M,
#' Natarajan K, Henry M, Winkler J, Marchan R, Stoppini L, Bosgra S, Westerhout
#' J, Verwei M, Vilo J, Kortenkamp A, Hescheler J, Hothorn L, Bremer S, van
#' Thriel C, Krause KH, Hengstler JG, Rahnenfuhrer J, Leist M, Sachinidis A
#' (2013). Human embryonic stem cell-derived test systems for developmental
#' neurotoxicity: a transcriptomics approach. Arch Toxicol. 87(1):123-43. PMID:
#' 23179753
#'
#' Leon-Reyes MR, Castaneda-Hernandez G, Ortiz MI (2009). Pharmacokinetic of
#' diclofenac in the presence and absence of glibenclamide in the rat. J Pharm
#' Pharm Sci. 12(3):280-7. PMID: 20067705
#'
#' Nagata M, Hidaka M, Sekiya H, Kawano Y, Yamasaki K, Okumura M, Arimori K
#' (2007). Effects of pomegranate juice on human cytochrome P450 2C9 and
#' tolbutamide pharmacokinetics in rats. Drug Metab Dispos. 35(2):302-5. PMID:
#' 17132763
#'
#' Okiyama M, Ueno K, Ohmori S, Igarashi T, Kitagawa H (1988). Drug
#' interactions between imipramine and benzodiazepines in rats. J Pharm Sci.
#' 77(1):56-63. PMID: 2894451
#'
#' Pelissier-Alicot AL, Schreiber-Deturmeny E, Simon N, Gantenbein M,
#' Bruguerolle B (2002). Time-of-day dependent pharmacodynamic and
#' pharmacokinetic profiles of caffeine in rats. Naunyn Schmiedebergs Arch
#' Pharmacol. 365(4):318-25. PMID: 11919657
#'
#' Piersma AH, Bosgra S, van Duursen MB, Hermsen SA, Jonker LR, Kroese ED, van
#' der Linden SC, Man H, Roelofs MJ, Schulpen SH, Schwarz M, Uibel F, van
#' Vugt-Lussenburg BM, Westerhout J, Wolterbeek AP, van der Burg B (2013).
#' Evaluation of an alternative in vitro test battery for detecting
#' reproductive toxicants. Reprod Toxicol. 38:53-64. PMID: 23511061
#'
#' Pollack GM, Li RC, Ermer JC, Shen DD (1985). Effects of route of
#' administration and repetitive dosing on the disposition kinetics of
#' di(2-ethylhexyl) phthalate and its mono-de-esterified metabolite in rats.
#' Toxicol Appl Pharmacol. Jun 30;79(2):246-56. PMID: 4002226
#'
#' Saadeddin A, Torres-Molina F, Carcel-Trullols J, Araico A, Peris JE (2004).
#' Pharmacokinetics of the time-dependent elimination of all-trans-retinoic
#' acid in rats. AAPS J. 6(1):1-9. PMID: 18465253
#'
#' Satterwhite JH, Boudinot FD (1991). Effects of age and dose on the
#' pharmacokinetics of ibuprofen in the rat. Drug Metab Dispos. 19(1):61-7.
#' PMID: 1673423
#'
#' Szymura-Oleksiak J, Panas M, Chrusciel W (1983). Pharmacokinetics of
#' imipramine after single and multiple intravenous administration in rats. Pol
#' J Pharmacol Pharm. 35(2):151-7. PMID: 6622297
#'
#' Tanaka C, Kawai R, Rowland M (2000). Dose-dependent pharmacokinetics of
#' cyclosporin A in rats: events in tissues. Drug Metab Dispos. 28(5):582-9.
#' PMID: 10772639
#'
#' Timchalk C, Nolan RJ, Mendrala AL, Dittenber DA, Brzak KA, Mattsson JL
#' (2002). A Physiologically based pharmacokinetic and pharmacodynamic
#' (PBPK/PD) model for the organophosphate insecticide chlorpyrifos in rats and
#' humans. Toxicol Sci. Mar;66(1):34-53. PMID: 11861971
#'
#' Tokuma Y, Sekiguchi M, Niwa T, Noguchi H (1988). Pharmacokinetics of
#' nilvadipine, a new dihydropyridine calcium antagonist, in mice, rats,
#' rabbits and dogs. Xenobiotica 18(1):21-8. PMID: 3354229
#'
#' Treiber A, Schneiter R, Delahaye S, Clozel M (2004). Inhibition of organic
#' anion transporting polypeptide-mediated hepatic uptake is the major
#' determinant in the pharmacokinetic interaction between bosentan and
#' cyclosporin A in the rat. J Pharmacol Exp Ther. 308(3):1121-9. PMID:
#' 14617681
#'
#' Tsui BC, Feng JD, Buckley SJ, Yeung PK (1994). Pharmacokinetics and
#' metabolism of diltiazem in rats following a single intra-arterial or single
#' oral dose. Eur J Drug Metab Pharmacokinet. 19(4):369-73. PMID: 7737239
#'
#' Wambaugh, John F., et al. "Toxicokinetic triage for environmental
#' chemicals." Toxicological Sciences (2015): 228-237.
#'
#' Wang Y, Roy A, Sun L, Lau CE (1999). A double-peak phenomenon in the
#' pharmacokinetics of alprazolam after oral administration. Drug Metab Dispos.
#' 27(8):855-9. PMID: 10421610
#'
#' Wang X, Lee WY, Or PM, Yeung JH (2010). Pharmacokinetic interaction studies
#' of tanshinones with tolbutamide, a model CYP2C11 probe substrate, using
#' liver microsomes, primary hepatocytes and in vivo in the rat. Phytomedicine.
#' 17(3-4):203-11. PMID: 19679455
#'
#' Yang SH, Lee MG (2008). Dose-independent pharmacokinetics of ondansetron in
#' rats: contribution of hepatic and intestinal first-pass effects to low
#' bioavailability. Biopharm Drug Dispos. 29(7):414-26. PMID: 18697186
#'
#' Yeung PK, Alcos A, Tang J (2009). Pharmacokinetics and Hemodynamic Effects
#' of Diltiazem in Rats Following Single vs Multiple Doses In Vivo. Open Drug
#' Metab J. 3:56-62.
#' @source Wambaugh et al. 2018 Toxicological Sciences, in press
#' @keywords data
"chem.invivo.PK.summary.data"
#' Parameter Estimates from Wambaugh et al. (2018)
#'
#' This table includes 1 and 2 compartment fits of plasma concentration vs time
#' data aggregated from chem.invivo.PK.data, performed in Wambaugh et al. 2018.
#' Data includes volume of distribution (Vdist, L/kg), elimination rate (kelim,
#' 1/h), gut absorption rate (kgutabs, 1/h), fraction absorbed (Fgutabs), and
#' steady state concentration (Css, mg/L).
#'
#'
#' @docType data
#' @format data.frame
#' @author John Wambaugh
#' @source Wambaugh et al. 2018 Toxicological Sciences, in press
#' @keywords data
"chem.invivo.PK.aggregate.data"
#' Raw Bayesian in vitro Toxicokinetic Data Analysis from Wambaugh et al. (2019)
#'
#' These data are the new HTTK in vitro data for chemicals reported in Wambaugh
#' et al. (2019) They
#' are the output of different Bayesian models evaluated to compare using a
#' single protein concentration vs. the new three concentration titration
#' protocol. These data summarize the results of Bayesian analysis of the in vitro
#' toxicokinetic experiments conducted by Cyprotex to characterize fraction
#' unbound in the presence of pooled human plasma protein and the intrnsic
#' hepatic clearance of the chemical by pooled human hepatocytes.
#' This file includes replicates (diferent CompoundName id's but same chemical')
#'
#' @format A data frame with 530 rows and 28 variables:
#' \describe{
#' \item{DTXSID}{Identifier for CompTox Chemical Dashboard}
#' \item{Name}{The name of the chemical}
#' \item{CAS}{The Chemical Abstracts Service Registry Number}
#' \item{CompoundName}{Sample name provided by EPA to Cyprotex}
#' \item{Fup.point}{Point estimate of the fraction of
#' chemical free in the presence of plasma}
#' \item{Base.Fup.Med}{Median of Bayesian credible interval for
#' fraction of chemical free in the presence of plasma for analysis of 100%
#' physiological plasma protein data only (base model)}
#' \item{Base.Fup.Low}{Lower 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma for analysis of 100%
#' physiological plasma protein data only (base model)}
#' \item{Base.Fup.High}{Upper 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma for analysis of 100%
#' physiological plasma protein data only (base model)}
#' \item{Affinity.Fup.Med}{Median of Bayesian credible interval for
#' fraction of chemical free in the presence of plasma for analysis of protein
#' titration protocol data (affinity model)}
#' \item{Affinity.Fup.Low}{Lower 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma for analysis of protein
#' titration protocol data (affinity model)}
#' \item{Affinity.Fup.High}{Upper 95th percentile of Bayesian credible
#' interval for fraction of chemical free in the presence of plasma for analysis of protein
#' titration protocol data (affinity model)}
#' \item{Affinity.Kd.Med}{Median of Bayesian credible interval for
#' protein binding affinity from analysis of protein
#' titration protocol data (affinity model)}
#' \item{Affinity.Kd.Low}{Lower 95th percentile of Bayesian credible
#' interval for protein binding affinity from analysis of protein
#' titration protocol data (affinity model)}
#' \item{Affinity.Kd.High}{Upper 95th percentile of Bayesian credible
#' interval for protein binding affinity from analysis of protein
#' titration protocol data (affinity model)}
#' \item{Decreases.Prob}{Probability that the chemical concentration decreased
#' systematiclally during hepatic clearance assay.}
#' \item{Saturates.Prob}{Probability that the rate of chemical concentration
#' decrease varied between the 1 and 10 uM hepatic clearance experiments.}
#' \item{Slope.1uM.Median}{Estimated slope for chemcial concentration decrease
#' in the 1 uM hepatic clearance assay.}
#' \item{Slope.10uM.Median}{Estimated slope for chemcial concentration decrease
#' in the 10 uM hepatic clearance assay.}
#' \item{CLint.1uM.Median}{Median of Bayesian credible interval for intrinsic
#' hepatic clearance at 1 uM initital chemical concentration (uL/min/million hepatocytes)]}
#' \item{CLint.1uM.Low95th}{Lower 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance at 1 uM initital chemical
#' concentration (uL/min/million hepatocytes)}
#' \item{CLint.1uM.High95th}{Uppper 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance at 1 uM initital chemical
#' concentration(uL/min/million hepatocytes)}
#' \item{CLint.10uM.Median}{Median of Bayesian credible interval for intrinsic
#' hepatic clearance at 10 uM initital chemical concentration (uL/min/million hepatocytes)]}
#' \item{CLint.10uM.Low95th}{Lower 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance at 10 uM initital chemical
#' concentration (uL/min/million hepatocytes)}
#' \item{CLint.10uM.High95th}{Uppper 95th percentile of Bayesian credible
#' interval for intrinsic hepatic clearance at 10 uM initital chemical
#' concentration(uL/min/million hepatocytes)}
#' \item{CLint.1uM.Point}{Point estimate of intrinsic hepatic clearance
#' (uL/min/million hepatocytes) for 1 uM initial chemical concentration}
#' \item{CLint.10uM.Point}{Point estimate of intrinsic hepatic clearance
#' (uL/min/million hepatocytes) for 10 uM initial chemical concentration}
#' \item{Fit}{Classification of clearance observed}
#' \item{SMILES}{Simplified Molecular-Input Line-Entry System structure
#' description}
#' }
#' @source Wambaugh et al. (2019)
#'
#'@keywords data
#'
#'@author John Wambaugh
#'
#'@references Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and
#'Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.
"wambaugh2019.raw"
#' NHANES Chemical Intake Rates for chemicals in Wambaugh et al. (2019)
#'
#' These data are a subset of the Bayesian inferrences reported by Ring et al.
#' (2017) from the U.S. Centers for Disease Control and Prevention (CDC)
#' National Health and Nutrition Examination Survey (NHANES). They reflect the
#' populaton median intake rate (mg/kg body weight/day), with uncertainty.
#'
#' @format A data frame with 20 rows and 4 variables:
#' \describe{
#' \item{lP}{The median of the Bayesian credible interval for median population
#' intake rate (mg/kg bodyweight/day)}
#' \item{lP.min}{The lower 95th percentile of the Bayesian credible interval for median population
#' intake rate (mg/kg bodyweight/day)}
#' \item{lP.max}{The upper 95th percentile of the Bayesian credible interval for median population
#' intake rate (mg/kg bodyweight/day)}
#' \item{CASRN}{The Chemical Abstracts Service Registry Number}
#' }
#' @source Wambaugh et al. (2019)
#'
#'@keywords data
#'
#'@author John Wambaugh
#'
#'@references Ring, Caroline L., et al. "Identifying populations sensitive to
#' evironmental chemicals by simulating toxicokinetic variability." Environment
#' international 106 (2017): 105-118
#'
#'@references Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and
#'Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.
"wambaugh2019.nhanes"
#' ExpoCast SEEM3 Consensus Exposure Model Predictions for Chemical Intake Rates
#'
#' These data are a subset of the Bayesian inferrences reported by Ring et al.
#' (2019) for a consensus model of twelve exposue predictors. The predictors
#' were calibrated based upon their ability to predict intake rates inferred
# 'from the U.S. Centers for Disease Control and Prevention (CDC)
#' National Health and Nutrition Examination Survey (NHANES). They reflect the
#' populaton median intake rate (mg/kg body weight/day), with uncertainty.
#'
#' @format A data frame with 385 rows and 38 variables:
#' @source Wambaugh et al. (2019)
#'
#'@keywords data
#'
#'@author John Wambaugh
#'
#'@references Ring, Caroline L., et al. "Consensus modeling of median chemical
#' intake for the US population based on predictions of exposure pathways."
#' Environmental science & technology 53.2 (2018): 719-732.
#'
#'@references Wambaugh et al. (2019) "Assessing Toxicokinetic Uncertainty and
#'Variability in Risk Prioritization", Toxicological Sciences, 172(2), 235-251.
"wambaugh2019.seem3"
#' Physico-chemical properties and in vitro measurements for toxicokinetics
#'
#' This data set contains the necessary information to make basic,
#' high-throughput toxicokinetic (HTTK) predictions for compounds, including
#' Funbound.plasma, molecular weight (g/mol), logP, logMA (membrane affinity),
#' intrinsic clearance(uL/min/10^6 cells), and pKa. These data have been
#' compiled from multiple sources, and can be used to parameterize a variety of
#' toxicokinetic models. See variable EPA.ref for information on the reference EPA.
#'
#' In some cases the rapid equilbrium dailysis method (Waters et al., 2008)
#' fails to yield detectable concentrations for the free fraction of chemical.
#' In those cases we assume the compound is highly bound (that is, Fup approaches
#' zero). For some calculations (for example, steady-state plasma concentration)
#' there is precendent (Rotroff et al., 2010) for using half the average limit
#' of detection, that is 0.005. We do not recomend using other models where
#' quantities like partition coefficients must be predicted using Fup. We also
#' do not recomend including the value 0.005 in training sets for Fup predictive
#' models.
#'
#' \strong{Note} that in some cases the \strong{Funbound.plasma} and the
#' \strong{intrinsic clearance} are
#' \emph{provided as a series of numbers separated by commas}. These values are the
#' result of Bayesian analysis and characterize a distribution: the first value
#' is the median of the distribution, while the second and third values are the
#' lower and upper 95th percentile (that is qunatile 2.5 and 97.5) respectively.
#' For intrinsic clearance a fourth value indicating a p-value for a decrease is
#' provided. Typically 4000 samples were used for the Bayesian analusis, such
#' that a p-value of "0" is equivale to "<0.00025". See Wambaugh et al. (2019)
#' for more details.
#'
#' Any one chemical compound \emph{may have multiple ionization equilibria}
#' (see Strope et al., 2018) may both for donating or accepting a proton (and
#' therefore changing charge state). If there are multiple equlibria of the same
#' type (donor/accept])the are concatonated by commas.
#'
#' All species-specific information is initially from experimental measurements.
#' The functions \code{\link{load_sipes2017}}, \code{\link{load_pradeep2020}},
#' and \code{\link{load_dawson2021}} may be used to add in silico, structure-based
#' predictions for many thousands of additional compounds to this table.
#'
#' @docType data
#' @format A data.frame containing 9411 rows and 54 columns.
#' \tabular{lll}{
#' \strong{Column Name} \tab \strong{Description} \tab \strong{Units} \cr
#' Compound \tab The preferred name of the chemical compound \tab none \cr
#' CAS\tab The preferred Chemical Abstracts Service Registry Number \tab none \cr
#' CAS.Checksum \tab A logical indicating whether the CAS number is valid \tab none \cr
#' DTXSID \tab DSSTox Structure ID
#' (\url{http://comptox.epa.gov/dashboard}) \tab none \cr
#' Formula \tab The proportions of atoms within the chemical compound \tab none \cr
#' SMILES.desalt \tab The simplified molecular-input line-entry system
#' structure \tab none \cr
#' All.Compound.Names \tab All names of the chemical as they occured in the
#' data \tab none \cr
#' logHenry \tab The log10 Henry's law constant \tab
#' log10(atmosphers*m^3/mole) \cr
#' logHenry.Reference \tab Reference for Henry's law constant \tab \cr
#' logP \tab The log10 octanol:water partition coefficient (PC)\tab log10 unitless ratio \cr
#' logP.Reference \tab Reference for logPow \tab \cr
#' logPwa \tab The log10 water:air PC \tab log10 unitless ratio \cr
#' logPwa.Reference \tab Reference for logPwa \tab \cr
#' logMA \tab The log10 phospholipid:water PC or
#' "Membrane affinity" \tab unitless ratio \cr
#' logMA.Reference \tab Reference for membrane affinity \tab \cr #' logWSol \tab The log10 water solubility \tab log10(mole/L) \cr
#' logWSol.Reference \tab Reference for logWsol \tab \cr
#' MP \tab The chemical compound melting point \tab degrees Celsius \cr
#' MP.Reference \tab Reference for melting point \tab \cr
#' MW \tab The chemical compound molecular weight \tab g/mol \cr
#' MW.Reference \tab Reference for molecular weight \tab \cr
#' pKa_Accept \tab The hydrogen acceptor equilibria concentrations
#' \tab logarithm \cr
#' pKa_Accept.Reference \tab Reference for pKa_Accept \tab \cr
#' pKa_Donor \tab The hydrogen acceptor equilibria concentrations
#' \tab logarithm \cr
#' pKa_Donor.Reference \tab Reference for pKa_Donor \tab \cr
#' All.Species \tab All species for which data were available \tab none \cr
#' DTXSID.Reference \tab Reference for DTXSID \tab \cr
#' Formula.Reference \tab Reference for chemical formulat \tab \cr
#' [SPECIES].Clint \tab (Primary hepatocyte suspension)
#' intrinsic hepatic clearance \tab uL/min/10^6 hepatocytes \cr
#' [SPECIES].Clint.pValue \tab Probability that there is no clearance observed. \tab none \cr
#' [SPECIES].Clint.pValue.Ref \tab Reference for Clint pValue \tab \cr
#' [SPECIES].Clint.Reference \tab Reference for Clint \tab \cr
#' [SPECIES].Fgutabs \tab Fraction of chemical absorbed from the
#' gut \tab unitless fraction \cr
#' [SPECIES].Fgutabs.Reference \tab Reference for Fgutabs \tab \cr
#' [SPECIES].Funbound.plasma \tab Chemical fraction unbound in presence of
#' plasma proteins \tab unitless fraction \cr
#' [SPECIES].Funbound.plasma.Ref\tab Reference for Funbound.plasma \tab \cr
#' [SPECIES].Rblood2plasma \tab Chemical concentration blood to plasma ratio \tab unitless ratio \cr
#' [SPECIES].Rblood2plasma.Ref \tab Reference for Rblood2plasma \tab \cr
#' SMILES.desalt.Reference"\tab Reference for SMILES structure \tab \cr
#' Chemical.Class \tab All classes to which the chemical has been assigned \tab \cr
#' }
#' @author John Wambaugh
#'
#' @references CompTox Chemicals Dashboard (\url{http://comptox.epa.gov/dashboard})
#'
#' EPI Suite, https://www.epa.gov/opptintr/exposure/pubs/episuite.htm
#'
#' Brown, Hayley S., Michael Griffin, and J. Brian Houston. "Evaluation of
#' cryopreserved human hepatocytes as an alternative in vitro system to
#' microsomes for the prediction of metabolic clearance." Drug metabolism and
#' disposition 35.2 (2007): 293-301.
#'
#' Gulden, Michael, et al. "Impact of protein binding on the availability and
#' cytotoxic potency of organochlorine pesticides and chlorophenols in vitro."
#' Toxicology 175.1-3 (2002): 201-213.
#'
#' Hilal, S., Karickhoff, S. and Carreira, L. (1995). A rigorous test for
#' SPARC's chemical reactivity models: Estimation of more than 4300 ionization
#' pKas. Quantitative Structure-Activity Relationships 14(4), 348-355.
#'
#' Honda, G. S., Pearce, R. G., Pham, L. L., Setzer, R. W., Wetmore, B. A.,
#' Sipes, N. S., ... & Wambaugh, J. F. (2019). Using the concordance of in
#' vitro and in vivo data to evaluate extrapolation assumptions. PloS one,
#' 14(5), e0217564.
#'
#' Ito, K. and Houston, J. B. (2004). Comparison of the use of liver models for
#' predicting drug clearance using in vitro kinetic data from hepatic
#' microsomes and isolated hepatocytes. Pharm Res 21(5), 785-92.
#'
#' Jones, O. A., Voulvoulis, N. and Lester, J. N. (2002). Aquatic environmental
#' assessment of the top 25 English prescription pharmaceuticals. Water
#' research 36(20), 5013-22.
#'
#' Jones, Barry C., et al. "An investigation into the prediction of in vivo
#' clearance for a range of flavin-containing monooxygenase substrates."
#' Drug metabolism and disposition 45.10 (2017): 1060-1067.
#'
#' Lau, Y. Y., Sapidou, E., Cui, X., White, R. E. and Cheng, K. C. (2002).
#' Development of a novel in vitro model to predict hepatic clearance using
#' fresh, cryopreserved, and sandwich-cultured hepatocytes. Drug Metabolism and
#' Disposition 30(12), 1446-54.
#'
#' Linakis, M. W., Sayre, R. R., Pearce, R. G., Sfeir, M. A., Sipes, N. S.,
#' Pangburn, H. A., ... & Wambaugh, J. F. (2020). Development and evaluation of
#' a high-throughput inhalation model for organic chemicals. Journal of
#' Exposure Science & Environmental Epidemiology, 1-12.
#'
#' Lombardo, F., Berellini, G., & Obach, R. S. (2018). Trend analysis of a
#' database of intravenous pharmacokinetic parameters in humans for 1352 drug
#' compounds. Drug Metabolism and Disposition, 46(11), 1466-1477.
#'
#' McGinnity, D. F., Soars, M. G., Urbanowicz, R. A. and Riley, R. J. (2004).
#' Evaluation of fresh and cryopreserved hepatocytes as in vitro drug
#' metabolism tools for the prediction of metabolic clearance. Drug Metabolism
#' and Disposition 32(11), 1247-53, 10.1124/dmd.104.000026.
#'
#' Naritomi, Y., Terashita, S., Kagayama, A. and Sugiyama, Y. (2003). Utility
#' of Hepatocytes in Predicting Drug Metabolism: Comparison of Hepatic
#' Intrinsic Clearance in Rats and Humans in Vivo and in Vitro. Drug Metabolism
#' and Disposition 31(5), 580-588, 10.1124/dmd.31.5.580.
#'
#' Obach, R. S. (1999). Prediction of human clearance of twenty-nine drugs from
#' hepatic microsomal intrinsic clearance data: An examination of in vitro
#' half-life approach and nonspecific binding to microsomes. Drug Metabolism
#' and Disposition 27(11), 1350-9.
#'
#' Paini, Alicia; Cole, Thomas; Meinero, Maria; Carpi, Donatella; Deceuninck,
#' Pierre; Macko, Peter; Palosaari, Taina; Sund, Jukka; Worth, Andrew; Whelan,
#' Maurice (2020): EURL ECVAM in vitro hepatocyte clearance and blood plasma
#' protein binding dataset for 77 chemicals. European Commission, Joint Research
#' Centre (JRC) [Dataset] PID: https://data.europa.eu/89h/a2ff867f-db80-4acf-8e5c-e45502713bee
#'
#' Paixao, P., Gouveia, L. F., & Morais, J. A. (2012). Prediction of the human
#' oral bioavailability by using in vitro and in silico drug related parameters
#' in a physiologically based absorption model. International journal of
#' pharmaceutics, 429(1), 84-98.
#'
#' Pirovano, Alessandra, et al. "QSARs for estimating intrinsic hepatic
#' clearance of organic chemicals in humans." Environmental toxicology and
#' pharmacology 42 (2016): 190-197.
#'
#' Riley, Robert J., Dermot F. McGinnity, and Rupert P. Austin. "A unified
#' model for predicting human hepatic, metabolic clearance from in vitro
#' intrinsic clearance data in hepatocytes and microsomes." Drug Metabolism and
#' Disposition 33.9 (2005): 1304-1311.
#'
#' Schmitt, W. (2008). General approach for the calculation of tissue to plasma
#' partition coefficients. Toxicology in vitro : an international journal
#' published in association with BIBRA 22(2), 457-67,
#' 10.1016/j.tiv.2007.09.010.
#'
#' Shibata, Y., Takahashi, H., Chiba, M. and Ishii, Y. (2002). Prediction of
#' Hepatic Clearance and Availability by Cryopreserved Human Hepatocytes: An
#' Application of Serum Incubation Method. Drug Metabolism and Disposition
#' 30(8), 892-896, 10.1124/dmd.30.8.892.
#'
#' Sohlenius-Sternbeck, Anna-Karin, et al. "Practical use of the regression
#' offset approach for the prediction of in vivo intrinsic clearance from
#' hepatocytes." Xenobiotica 42.9 (2012): 841-853.
#'
#' Tonnelier, A., Coecke, S. and Zaldivar, J.-M. (2012). Screening of chemicals
#' for human bioaccumulative potential with a physiologically based
#' toxicokinetic model. Archives of Toxicology 86(3), 393-403,
#' 10.1007/s00204-011-0768-0.
#'
#' Uchimura, Takahide, et al. "Prediction of human blood-to-plasma drug
#' concentration ratio." Biopharmaceutics & drug disposition 31.5-6 (2010):
#' 286-297.
#'
#' Wambaugh, J. F., Wetmore, B. A., Ring, C. L., Nicolas, C. I., Pearce, R. G.,
#' Honda, G. S., ... & Badrinarayanan, A. (2019). Assessing Toxicokinetic
#' Uncertainty and Variability in Risk Prioritization. Toxicological Sciences,
#' 172(2), 235-251.
#'
#' Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Sochaski, M. A., Rotroff,
#' D. M., Freeman, K., Clewell, H. J., 3rd, Dix, D. J., Andersen, M. E., Houck,
#' K. A., Allen, B., Judson, R. S., Singh, R., Kavlock, R. J., Richard, A. M.
#' and Thomas, R. S. (2012). Integration of dosimetry, exposure, and
#' high-throughput screening data in chemical toxicity assessment.
#' Toxicological sciences : an official journal of the Society of Toxicology
#' 125(1), 157-74, 10.1093/toxsci/kfr254.
#'
#' Wetmore, B. A., Wambaugh, J. F., Ferguson, S. S., Li, L., Clewell, H. J.,
#' Judson, R. S., Freeman, K., Bao, W., Sochaski, M. A., Chu, T.-M., Black, M.
#' B., Healy, E., Allen, B., Andersen, M. E., Wolfinger, R. D. and Thomas, R.
#' S. (2013). Relative Impact of Incorporating Pharmacokinetics on Predicting
#' In Vivo Hazard and Mode of Action from High-Throughput In Vitro Toxicity
#' Assays. Toxicological Sciences 132(2), 327-346, 10.1093/toxsci/kft012.
#'
#' Wetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S. S., Sochaski, M.
#' A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K., Judson, R. S.,
#' LeCluyse, E., Clewell, H.J. III, Thomas, R.S., and Andersen, M. E. (2015).
#' "Incorporating High-Throughput Exposure Predictions with Dosimetry-Adjusted
#' In Vitro Bioactivity to Inform Chemical Toxicity Testing" Toxicological
#' Sciences, kfv171.
#'
#' F. L. Wood, J. B. Houston and D. Hallifax
#' 'Drug Metabolism and Disposition November 1, 2017, 45 (11) 1178-1188;
#' DOI: https://doi.org/10.1124/dmd.117.077040
#' @source Wambaugh, John F., et al. "Toxicokinetic triage for environmental
#' chemicals." Toxicological Sciences (2015): 228-237.
#' @keywords data
"chem.physical_and_invitro.data"
#' Tox21 2015 Active Hit Calls (EPA)
#'
#' The ToxCast and Tox21 research programs employ batteries of high-throughput
#' assays to assess chemical bioactivity in vitro. Not every chemical is tested
#' through every assay. Most assays are conducted in concentration response,
#' and each corresponding assay endpoint is analyzed statistically to determine
#' if there is a concentration-dependent response or "hit" using the ToxCast
#' Pipeline. Most assay endpoint-chemical combinations are non-responsive.
#' Here, only the hits are treated as potential indicators of bioactivity. This
#' bioactivity does not have a direct toxicological interpretation. The October
#' 2015 release (invitrodb_v2) of the ToxCast and Tox21 data were used for this
#' analysis. This object contains just the chemicals in Wambaugh et al. (2019)
#' and only the quantiles across all assays for the ACC.
#'
#' @name wambaugh2019.tox21
#' @docType data
#' @format A data.table with 401 rows and 6 columns
#' @author John Wambaugh
#' @references Kavlock, Robert, et al. "Update on EPA's ToxCast program:
#' providing high-throughput decision support tools for chemical risk
#' management." Chemical research in toxicology 25.7 (2012): 1287-1302.
#'
#' Tice, Raymond R., et al. "Improving the human hazard characterization of
#' chemicals: a Tox21 update." Environmental health perspectives 121.7 (2013):
#' 756-765.
#'
#' Richard, Ann M., et al. "ToxCast chemical landscape: paving the road to 21st
#' century toxicology." Chemical research in toxicology 29.8 (2016): 1225-1251.
#'
#' Filer, Dayne L., et al. "tcpl: the ToxCast pipeline for high-throughput
#' screening data." Bioinformatics 33.4 (2016): 618-620.
#'
#' Wambaugh, John F., et al. "Assessing Toxicokinetic Uncertainty and
#' Variability in Risk Prioritization." Toxicological Sciences 172.2 (2019):
#' 235-251.
#'
#'
#' @source \url{ftp://newftp.epa.gov/COMPTOX/High_Throughput_Screening_Data/Previous_Data/ToxCast_Data_Release_Oct_2015/}
#' @keywords data
"wambaugh2019.tox21"
#' Howgate 2006
#'
#' This data set is only used in Vignette 5.
#'
#' @docType data
#' @format A data.table containing 24 rows and 11 columns.
#' @keywords data
#' @author Caroline Ring
#' @references
#' Howgate, E. M., et al. "Prediction of in vivo drug clearance from in vitro
#' data. I: impact of inter-individual variability." Xenobiotica 36.6 (2006):
#' 473-497.
"howgate"
#' Johnson 2006
#'
#' This data set is only used in Vignette 5.
#'
#' @docType data
#'
#' @format A data.table containing 60 rows and 11 columns.
#'
#' @author Caroline Ring
#' @references
#' Johnson, Trevor N., Amin Rostami-Hodjegan, and Geoffrey T. Tucker.
#' "Prediction of the clearance of eleven drugs and associated variability in
#' neonates, infants and children." Clinical pharmacokinetics 45.9 (2006):
#' 931-956.
#'
#' @keywords data
"johnson"
#' Published Pharmacokinetic Parameters from Obach et al. 2008
#'
#' This data set is used in Vignette 4 for steady state concentration.
#'
#'
#' @docType data
#' @format A data.frame containing 670 rows and 8 columns.
#' @references Obach, R. Scott, Franco Lombardo, and Nigel J. Waters. "Trend
#' analysis of a database of intravenous pharmacokinetic parameters in humans
#' for 670 drug compounds." Drug Metabolism and Disposition 36.7 (2008):
#' 1385-1405.
#' @keywords data
"Obach2008"
#' NHANES Exposure Data
#'
#' This data set is only used in Vignette 6.
#'
#' @docType data
#'
#' @format A data.table containing 1060 rows and 5 columns.
#'
#' @author Caroline Ring
#'
#' @references
#' Wambaugh, John F., et al. "High throughput heuristics for prioritizing human
#' exposure to environmental chemicals." Environmental science & technology
#' 48.21 (2014): 12760-12767.
#'
#' @keywords data
"onlyp"
#' Partition Coefficient Data
#'
#' Measured rat in vivo partition coefficients and data for predicting them.
#'
#'
#' @docType data
#' @format A data.frame.
#' @author Jimena Davis and Robert Pearce
#' @references Schmitt, W., General approach for the calculation of tissue to
#' plasma partition coefficients. Toxicology in Vitro, 2008. 22(2): p. 457-467.
#'
#' Schmitt, W., Corrigendum to:"General approach for the calculation of tissue
#' to plasma partition coefficients"[Toxicology in Vitro 22 (2008) 457-467].
#' Toxicology in Vitro, 2008. 22(6): p. 1666.
#'
#' Poulin, P. and F.P. Theil, A priori prediction of tissue: plasma partition
#' coefficients of drugs to facilitate the use of physiologically based
#' pharmacokinetic models in drug discovery. Journal of pharmaceutical
#' sciences, 2000. 89(1): p. 16-35.
#'
#' Rodgers, T. and M. Rowland, Physiologically based pharmacokinetic modelling
#' 2: predicting the tissue distribution of acids, very weak bases, neutrals
#' and zwitterions. Journal of pharmaceutical sciences, 2006. 95(6): p.
#' 1238-1257.
#'
#' Rodgers, T., D. Leahy, and M. Rowland, Physiologically based pharmacokinetic
#' modeling 1: predicting the tissue distribution of moderate-to-strong bases.
#' Journal of pharmaceutical sciences, 2005. 94(6): p. 1259-1276.
#'
#' Rodgers, T., D. Leahy, and M. Rowland, Tissue distribution of basic drugs:
#' Accounting for enantiomeric, compound and regional differences amongst
#' beta-blocking drugs in rat. Journal of pharmaceutical sciences, 2005. 94(6):
#' p. 1237-1248.
#'
#' Gueorguieva, I., et al., Development of a whole body physiologically based
#' model to characterise the pharmacokinetics of benzodiazepines. 1: Estimation
#' of rat tissue-plasma partition ratios. Journal of pharmacokinetics and
#' pharmacodynamics, 2004. 31(4): p. 269-298.
#'
#' Poulin, P., K. Schoenlein, and F.P. Theil, Prediction of adipose tissue:
#' plasma partition coefficients for structurally unrelated drugs. Journal of
#' pharmaceutical sciences, 2001. 90(4): p. 436-447.
#'
#' Bjorkman, S., Prediction of the volume of distribution of a drug: which
#' tissue-plasma partition coefficients are needed? Journal of pharmacy and
#' pharmacology, 2002. 54(9): p. 1237-1245.
#'
#' Yun, Y. and A. Edginton, Correlation-based prediction of tissue-to-plasma
#' partition coefficients using readily available input parameters.
#' Xenobiotica, 2013. 43(10): p. 839-852.
#'
#' Uchimura, T., et al., Prediction of human blood-to-plasma drug concentration
#' ratio. Biopharmaceutics & drug disposition, 2010. 31(5-6): p. 286-297.
#' @keywords data
"pc.data"
#' Species-specific physiology parameters
#'
#' This data set contains values from Davies and Morris (1993) necessary to
#' paramaterize a toxicokinetic model for human, mouse, rat, dog, or rabbit.
#' The temperature for each species are taken from Robertshaw et al. (2004),
#' Gordon (1993), and Stammers(1926).
#'
#'
#' @docType data
#' @format A data.frame containing 11 rows and 7 columns.
#' @author John Wambaugh and Nisha Sipes
#' @references Davies, B. and Morris, T. (1993). Physiological Parameters in
#' Laboratory Animals and Humans. Pharmaceutical Research 10(7), 1093-1095,
#' 10.1023/a:1018943613122. %gfr and other flows Anderson and Holford (2009)
#' %scaling gfr by 3/4 Robertshaw, D., Temperature Regulation and Thermal
#' Environment, in Dukes' Physiology of Domestic Animals, 12th ed., Reece W.O.,
#' Ed. Copyright 2004 by Cornell University. Stammers (1926) The blood count
#' and body temperature in normal rats Gordon (1993) Temperature Regulation in
#' Laboratory Rodents
#' @source Wambaugh, John F., et al. "Toxicokinetic triage for environmental
#' chemicals." Toxicological Sciences (2015): 228-237.
#' @keywords data
"physiology.data"
#' Tissue composition and species-specific physiology parameters
#'
#' 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
#'
#' New tissues can be added to this table to generate
#' their partition coefficients.
#'
#' The tissue data needed for calculating partition coefficients include:
#' cellular and water fractions of
#' total volume, lipid and protein fractions of cellular volume, lipid
#' fractions of the total lipid volume, the pH of each tissue,
#' and the fractional volume of protein in plasma.
#'
#' @seealso \code{\link{predict_partitioning_schmitt}}
#'
#' @docType data
#' @format A data.frame containing 13 rows and 20 columns.
#' @author John Wambaugh, Robert Pearce, and Nisha Sipes
#' @references Birnbaum, L and Brown, R and Bischoff, K and Foran, J and
#' Blancato, J and Clewell, H and Dedrick, R (1994). Physiological parameter
#' values for PBPK model. International Life Sciences Institute, Risk Science
#' Institute, Washington, DC
#'
#' Ruark, Christopher D., et al. "Predicting passive and active tissue: plasma
#' partition coefficients: Interindividual and interspecies variability."
#' Journal of pharmaceutical sciences 103.7 (2014): 2189-2198.
#'
#' Schmitt, W. (2008). General approach for the calculation of tissue to plasma
#' partition coefficients. Toxicology in vitro : an international journal
#' published in association with BIBRA 22(2), 457-67,
#' 10.1016/j.tiv.2007.09.010.
#'
#' ICRP. Report of the Task Group on Reference Man. ICRP Publication 23 1975
#' @source Pearce et al. (2017), in preparation,
#'
#' Wambaugh, John F., et al. "Toxicokinetic triage for environmental
#' chemicals." Toxicological Sciences (2015): 228-237.
#'
#' @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)
#'
#' @keywords data
"tissue.data"
#' Published toxicokinetic predictions based on in vitro data from Wetmore et
#' al. 2012.
#'
#' This data set overlaps with Wetmore.data and is used only in Vignette 4 for
#' steady state concentration.
#'
#'
#' @docType data
#' @format A data.frame containing 13 rows and 15 columns.
#' @references Wetmore, B.A., Wambaugh, J.F., Ferguson, S.S., Sochaski, M.A.,
#' Rotroff, D.M., Freeman, K., Clewell, H.J., Dix, D.H., Andersen, M.E., Houck,
#' K.A., Allen, B., Judson, R.S., Sing, R., Kavlock, R.J., Richard, A.M., and
#' Thomas, R.S., "Integration of Dosimetry, Exposure and High-Throughput
#' Screening Data in Chemical Toxicity Assessment," Toxicological Sciences 125
#' 157-174 (2012)
#' @keywords data
"Wetmore2012"
#' Metabolism data involved in Linakis 2020 vignette analysis.
#'
#'
#' @docType data
#' @format A data.frame containing x rows and y columns.
#' @author Matt Linakis
#' @references DSStox database (https:// www.epa.gov/ncct/dsstox
#'
#' @source Matt Linakis
#' @keywords data
"metabolism_data_Linakis2020"
#' Concentration data involved in Linakis 2020 vignette analysis.
#'
#'
#' @docType data
#' @format A data.frame containing x rows and y columns.
#' @author Matt Linakis
#' @references DSStox database (https:// www.epa.gov/ncct/dsstox
#'
#' @source Matt Linakis
#' @keywords data
"concentration_data_Linakis2020"
#' Supplementary output from Linakis 2020 vignette analysis.
#'
#'
#' @docType data
#' @format A data.frame containing x rows and y columns.
#' @author Matt Linakis
#' @references DSStox database (https:// www.epa.gov/ncct/dsstox
#'
#' @source Matt Linakis
#' @keywords data
"supptab1_Linakis2020"
#' More supplementary output from Linakis 2020 vignette analysis.
#'
#'
#' @docType data
#' @format A data.frame containing x rows and y columns.
#' @author Matt Linakis
#' @references DSStox database (https:// www.epa.gov/ncct/dsstox
#'
#' @source Matt Linakis
#' @keywords data
"supptab2_Linakis2020"
#' Literature In Vivo Data on Doses Causing Neurological Effects
#'
#' Studies were selected from Table 1 in Mundy et al., 2015, as
#' the studies in that publication were cited as examples of
#' compounds with evidence for developmental neurotoxicity. There
#' were sufficient in vitro toxicokinetic data available for this
#' package for only 6 of the 42 chemicals.
#'
#' @docType data
#'
#' @format A data.frame containing 14 rows and 16 columns.
#'
#' @author Timothy J. Shafer
#'
#' @references
#' Frank, Christopher L., et al. "Defining toxicological tipping points
#' in neuronal network development." Toxicology and Applied
#' Pharmacology 354 (2018): 81-93.
#'
#' Mundy, William R., et al. "Expanding the test set: Chemicals with
#' potential to disrupt mammalian brain development." Neurotoxicology
#' and Teratology 52 (2015): 25-35.
#'
#' @keywords data
"Frank2018invivo"
#' Pearce et al. 2017 data
#'
#' This table includes the adjusted and unadjusted regression parameter
#' estimates for the chemical-specifc plasma
#' protein unbound fraction (fup) in 12 different tissue types.
#'
#' Predictions were made with regression models,
#' as reported in Pearce et al. (2017).
#'
#' @name pearce2017regression
#' @aliases Pearce2017Regression
#' @docType data
#' @format data.frame
#' @author Robert G. Pearce
#' @references 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.
#' @source Pearce et al. 2017 Regression Models
#' @keywords data
"pearce2017regression"
#' Dawson et al. 2021 data
#'
#' This table includes QSAR (Random Forest) model predicted values for unbound
#' fraction plasma protein (fup) and intrinsic hepatic clearance (clint) for a
#' subset of chemicals in the Tox21 library
#' (see \url{https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21}).
#'
#' Predictions were made with a set of Random Forest QSAR models,
#' as reported in Dawson et al. (2021).
#'
#' @name dawson2021
#' @aliases Dawson2021
#' @docType data
#' @format data.frame
#' @author Daniel E. Dawson
#' @references Dawson, Daniel E. et al. "Designing QSARs for parameters
#' of high-throughput toxicokinetic models using open-source descriptors."
#' Environmental Science & Technology____. (2021):______.
#' @source Dawson et al. 2021 Random Forest QSAR Model
#' @keywords data
"dawson2021"
#' Kapraun et al. 2019 data
#'
#' A list object containing time-varying parameters for the human maternal-fetal
#' HTTK model. List elements contain scalar coefficients for the polynomial,
#' logistic, Gompertz, and other functions of time describing blood flow rates,
#' tissue volumes, hematocrits, and other anatomical/physiological quantities
#' that change in the human mother and her fetus during pregnancy and gestation.
#'
#' @name kapraun2019
#' @aliases Kapraun2019
#' @docType data
#' @format list
#' @author Dustin F. Kapraun
#' @references
#' \insertRef{kapraun2019empirical}{httk}
#' @source Kapraun et al. 2019 Fetal PBTK Model
#' @keywords data
"kapraun2019"
#' Pradeep et al. 2020
#'
#' This table includes Support Vector Machine and Random Forest model predicted
#' values for unbound fraction plasma protein (fup) and intrinsic hepatic
#' clearance (clint) values for a subset of chemicals in the Tox21 library
#' (see \url{https://www.epa.gov/chemical-research/toxicology-testing-21st-century-tox21}).
#'
#' Prediction were made with Support Vector Machine and Random Forest models,
#' as reported in Pradeep et al. (2020).
#'
#' @name pradeep2020
#' @aliases Pradeep2020
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{pradeep2020chemstr}{httk}
#' @source Pradeep et al. 2020 Chemical Structure Predictive Models for HTTK
#' @keywords data
"pradeep2020"
#' Aylward et al. 2014
#'
#' Aylward et al. (2014) compiled measurements of the ratio of maternal to fetal
#' cord blood chemical concentrations at birth for a range of chemicals with
#' environmental routes of exposure, including bromodiphenyl ethers, fluorinated
#' compounds, organochlorine pesticides, polyaromatic hydrocarbons, tobacco smoke
#' components, and vitamins.
#'
#' @name aylward2014
#' @aliases Aylward2014
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{Aylward2014matfet}{httk}
#' @source Kapraun et al. 2021 (submitted)
#' @keywords data
"aylward2014"
#' AUCs for Pregnant and Non-Pregnant Women
#'
#' Dallmann et al. (2018) includes compiled literature descriptions of
#' toxicokinetic summary statistics, including time-integrated plasma
#' concentrations (area under the curve or AUC) for drugs administered to a
#' sample of subjects including both pregnant and non-pregnant women. The
#' circumstances of the dosing varied slightly between drugs and are summarized
#' in the table.
#'
#' @name pregnonpregaucs
#' @aliases pregnonpregaucs
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{dallmann2018pregpbtk}{httk}
#' @source Kapraun et al. 2021 (submitted)
#' @keywords data
"pregnonpregaucs"
#' Partition Coefficients from PK-Sim
#'
#' Dallmann et al. (2018) made use of PK-Sim to predict chemical- and tissue-
#' specific partition coefficients. The methods include both the default
#' PK-Sim approach and PK-Sim Standard and Rodgers & Rowland (2006).
#'
#' @name pksim.pcs
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{dallmann2018pregpbtk}{httk}
#' @source Kapraun et al. 2021 (submitted)
#' @keywords data
"pksim.pcs"
#' Fetal Partition Coefficients
#'
#' Partition coefficients were measured for tissues, including placenta, in
#' vitro by Csanady et al. (2002) for Bisphenol A and Diadzen. Curley et al.
#' (1969) measured the concentration of a variety of pesticides in the cord
#' blood of newborns and in the tissues of infants that were stillborn.
#'
#' Three of the chemicals studied by Curley et al. (1969) were modeled by
#' Weijs et al. (2013) using the same partition coefficients for mother and
#' fetus. The values used represented "prior knowledge" summarizing the
#' available literature.
#'
#' @name fetalpcs
#' @aliases fetalPCs
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{Csanady2002fetalpc}{httk}
#' \insertRef{Curley1969fetalpc}{httk}
#' \insertRef{Weijs2013fetalpc}{httk}
#' @source Kapraun et al. 2021 (submitted)
#' @keywords data
"fetalpcs"
#' Wang et al. 2018
#' Wang et al. (2018) screened the blood of 75 pregnant women for the presence
#' of environmental organic acids (EOAs) and identified mass spectral features
#' corresponding to 453 chemical formulae of which 48 could be mapped to likely
#' structures. Of the 48 with tentative structures the identity of six were
#' confirmed with available chemical standards.
#' @name wang2018
#' @aliases Wang2018
#' @docType data
#' @format data.frame
#' @references
#' \insertRef{Wang2018matbloodnta}{httk}
#' @source Kapraun et al. 2021 (submitted)
#' @keywords data
"wang2018"
#' ToxCast Example Data
#' The main page for the ToxCast data is here:
#' https://www.epa.gov/chemical-research/exploring-toxcast-data-downloadable-data
#' Most useful to us is a single file containing all the hits across all chemcials
#' and assays:
#' https://clowder.edap-cluster.com/datasets/6364026ee4b04f6bb1409eda?space=62bb560ee4b07abf29f88fef
#'
#' As of November, 2022 the most recent version was 3.5 and was available as an
#' .Rdata file (invitrodb_3_5_mc5.Rdata)
#'
#' Unfortunately for this vignette there are too many ToxCast data to fit into a
#' 5mb R package. So we will subset to just the shemicals for the
#' "Intro to IVIVE" vignette and distribute
#' only those data. In addition, out of 78 columns in the data, we will keep only
#' eight.
#' @name example.toxcast
#' @docType data
#' @format data.frame
#' @keywords data
"example.toxcast"
#' SEEM Example Data
#' We can grab SEEM daily intake rate predictions already in RData format from
#' https://github.com/HumanExposure/SEEM3RPackage/tree/main/SEEM3/data
#' Download the file Ring2018Preds.RData
#'
#' We do not have the space to distribute all the SEEM predictions within
#' this R package, but we can give you our "Intro to IVIVE" example chemicals
#' @name example.seem
#' @docType data
#' @format data.frame
#' @keywords data
"example.seem"
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.