dawson2023 {httk} | R Documentation |
Machine Learning PFAS Half-Life Predictions from Dawson et al. 2023
Description
Dawson et al. (2023) Supplemental Information S3 includes half-life predictions for 6603 PFAS, of which 3890 are estimated to be within the applicability domain (AD) for humans. This machine learning (ML) model predicts PFAS half-life as one of four categories. The ML model was trained to a dataset of 91 in vivo measured TK half-lives across 11 PFAS, 4 species, and two sexes. Predictions were a function of compound-specific physico-chemical descriptors, species-specific physiological descriptors, and an indicator variable for sex. The kinetics of PFAS are thought to be complicated by active transport, both through either proximal tubular resorption (into the blood) (Andersen et al. 2006) or secretion (into the urine) (Kudo et al. 2002). The ML model uses several species- and structure-derived surrogates for estimating the likelihood of active PFAS transport. Geometry of the proximal tubule was a surrogate for transporter expression: since secretion/resorption transporters line the surface of the proximal tubule, the amount of surface area provides an upper limit on the amount of transporter expression. PFAS similarity to three distinct endogenous ligands was considered as a surrogate for transporter affinity.
Usage
dawson2023
Format
data.frame
Details
The Dawson et al. (2023) half-life categories are:
Category | Range of Half-Lives |
1 | < 12 hours |
2 | < 1 week |
3 | < 2 months |
4 | > 2 months |
The data.frame contains the following columns:
Column Name | Description |
DTXSID | CompTox Chemicals Dashboard substance identifier |
Species | Species for which the prediction was made |
Sex | Sex for which the prediction was made |
DosingAdj | Route of dose administration -- intravenous, oral, or other |
ClassPredFull | The predicted half-life class (category) |
ClassModDomain | AD estimated from chemical classes of training set |
AMAD | AD including AD predicted for each model used for descriptors |
References
Dawson DE, Lau C, Pradeep P, Sayre RR, Judson RS, Tornero-Velez R, Wambaugh JF (2023). “A machine learning model to estimate toxicokinetic half-lives of per-and polyfluoro-alkyl substances (PFAS) in multiple species.” Toxics, 11(2), 98.
Andersen ME, Clewell III HJ, Tan Y, Butenhoff JL, Olsen GW (2006). “Pharmacokinetic modeling of saturable, renal resorption of perfluoroalkylacids in monkeys?probing the determinants of long plasma half-lives.” Toxicology, 227(1-2), 156–164.
Kudo N, Katakura M, Sato Y, Kawashima Y (2002). “Sex hormone-regulated renal transport of perfluorooctanoic acid.” Chemico-biological interactions, 139(3), 301–316.