honda2023.qspr {httk}

Predicted Caco-2 Apical-Basal Permeabilities

Description

Honda et al. (2023) describes the construction of a machine-learning quantitative structure-property relationship (QSPR )model for in vitro Caco-2 membrane permeabilites. That model was used to make chemical-specific predictions provided in this table.

Usage

honda2023.qspr

Format

An object of class data.frame with 14033 rows and 5 columns.

Details

Column Name	Description	Units
DTXSID	EPA's DSSTox Structure ID (https://comptox.epa.gov/dashboard)
Pab.Class.Pred	Predicted Pab rate of slow (1), moderate (2), or fast (3)
Pab.Pred.AD	Whether (1) or not (0) the chemical is anticipated to be withing the QSPR domain of applicability
CAS	Chemical Abstracts Service Registry Number
Pab.Quant.Pred	Median and 95-percent interval for values within the predicted class's training data moderate (2), or fast (3)	10^-6 cm/s

References

Honda G, Kenyon EM, Davidson-Fritz SE, Dinallo R, El-Masri H, Korel-Bexell E, Li L, Paul-Friedman K, Pearce R, Sayre R, Strock C, Thomas R, Wetmore BA, Wambaugh JF (2023). “Impact of Gut Permeability on Estimation of Oral Bioavailability for Chemicals in Commerce and the Environment.” Unpublished.

See Also

load_honda2023