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.
dawson2023data.frame
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 |
dawson2023machinehttk
andersen2006pharmacokinetichttk
kudo2002sexhttk