chem.physical_and_invitro.data: Physico-chemical properties and in vitro measurements for...

In httk: High-Throughput Toxicokinetics

Physico-chemical properties and in vitro measurements for toxicokinetics

Description

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.

Usage

chem.physical_and_invitro.data

Format

A data.frame containing 9411 rows and 54 columns.

Column Name	Description	Units
Compound	The preferred name of the chemical compound	none
CAS	The preferred Chemical Abstracts Service Registry Number	none
CAS.Checksum	A logical indicating whether the CAS number is valid	none
DTXSID	DSSTox Structure ID (http://comptox.epa.gov/dashboard)	none
Formula	The proportions of atoms within the chemical compound	none
SMILES.desalt	The simplified molecular-input line-entry system structure	none
All.Compound.Names	All names of the chemical as they occured in the data	none
logHenry	The log10 Henry's law constant	log10(atmosphers*m^3/mole)
logHenry.Reference	Reference for Henry's law constant
logP	The log10 octanol:water partition coefficient (PC)	log10 unitless ratio
logP.Reference	Reference for logPow
logPwa	The log10 water:air PC	log10 unitless ratio
logPwa.Reference	Reference for logPwa
logMA	The log10 phospholipid:water PC or "Membrane affinity"	unitless ratio
logMA.Reference	Reference for membrane affinity
#' logWSol	The log10 water solubility	log10(mole/L)
logWSol.Reference	Reference for logWsol
MP	The chemical compound melting point	degrees Celsius
MP.Reference	Reference for melting point
MW	The chemical compound molecular weight	g/mol
MW.Reference	Reference for molecular weight
pKa_Accept	The hydrogen acceptor equilibria concentrations	logarithm
pKa_Accept.Reference	Reference for pKa_Accept
pKa_Donor	The hydrogen acceptor equilibria concentrations	logarithm
pKa_Donor.Reference	Reference for pKa_Donor
All.Species	All species for which data were available	none
DTXSID.Reference	Reference for DTXSID
Formula.Reference	Reference for chemical formulat
[SPECIES].Clint	(Primary hepatocyte suspension) intrinsic hepatic clearance	uL/min/10^6 hepatocytes
[SPECIES].Clint.pValue	Probability that there is no clearance observed.	none
[SPECIES].Clint.pValue.Ref	Reference for Clint pValue
[SPECIES].Clint.Reference	Reference for Clint
[SPECIES].Fgutabs	Fraction of chemical absorbed from the gut	unitless fraction
[SPECIES].Fgutabs.Reference	Reference for Fgutabs
[SPECIES].Funbound.plasma	Chemical fraction unbound in presence of plasma proteins	unitless fraction
[SPECIES].Funbound.plasma.Ref	Reference for Funbound.plasma
[SPECIES].Rblood2plasma	Chemical concentration blood to plasma ratio	unitless ratio
[SPECIES].Rblood2plasma.Ref	Reference for Rblood2plasma
SMILES.desalt.Reference"	Reference for SMILES structure
Chemical.Class	All classes to which the chemical has been assigned

Details

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.

Note that in some cases the Funbound.plasma and the intrinsic clearance are 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 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 load_sipes2017, load_pradeep2020, and load_dawson2021 may be used to add in silico, structure-based predictions for many thousands of additional compounds to this table.

Author(s)

John Wambaugh

Source

Wambaugh, John F., et al. "Toxicokinetic triage for environmental chemicals." Toxicological Sciences (2015): 228-237.

References

CompTox Chemicals Dashboard (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

httk documentation built on March 7, 2023, 7:26 p.m.