Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Development of In Silico Tools to Predict the Behavior of Per- and Polyfluoroalkyl Substances (PFAS) in Biological Systems

Cheng, Weixiao (2021) Development of In Silico Tools to Predict the Behavior of Per- and Polyfluoroalkyl Substances (PFAS) in Biological Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

Primary Text

Download (4MB) | Preview


Per and polyfluoroalkyl substances (PFAS) are a group of chemicals that have been widely used in industrial and consumer products for decades. Recent estimates suggest there are over 4000 PFAS on the global market. However, many of these have very little information available about their potential hazards. Given the vast number of PFAS, a three-level hierarchical framework that includes permeability-limited physiologically based toxicokinetic (PBTK) model, molecular dynamics (MD) based workflow and machine learning (ML) based quantitative structure-activity relationships (QSAR) was proposed to inform the toxicokinetics, bioaccumulation and toxicity of PFAS. The PBTK model was developed to estimate the toxicokinetic and tissue distribution of perfluorooctanoic acid (PFOA) in male rats; the hierarchical Bayesian analysis was used to reduce the uncertainty of parameters and improve the robustness of the PBTK model. By comparing with different experimental studies, most of the predicted plasma toxicokinetic (e.g., half-life) and tissue distribution fell well within a factor of 2.0 of the measured data.
Moreover, a modeling workflow that combines molecular docking and MD simulation techniques was developed to estimate the binding affinity of PFAS for liver-type fatty acid binding protein (LFABP). The results suggest that EEA and ADONA are at least as strongly bound to rat LFABP as perfluoroheptanoic acid (PFHpA), and to human LFABP as PFOA; both F-53 and F-53B have similar or stronger binding affinities than perfluorooctane sulfonate (PFOS). In addition, human, rat, chicken, and rainbow trout had similar binding affinities to one another for each tested PFAS, whereas Japanese medaka and fathead minnow had significantly weaker LFABP binding affinity for some PFAS.
Finally, the ML-based QSAR model was developed to predict the bioactivity of around 4000 PFAS from the OECD report. Based on the collected PFAS dataset, a total of 5 different machine learning models were trained and validated that cover a variety of conventional models (i.e., logistic regression, random forest and multitask neural network) and advanced graph-based models (i.e., graph convolutional network and weave model). The model indicated that most of the biologically active PFAS have perfluoroalkyl chain lengths less than 12 and are categorized into fluorotelomer-related compounds and perfluoroalkyl acids.


Social Networking:
Share |


Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Cheng, Weixiaocheng_wx@pitt.eduWEC68
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairNg,
Committee MemberVidic,
Committee MemberKhanna,
Committee MemberAkcakaya,
Date: 3 September 2021
Date Type: Publication
Defense Date: 29 June 2021
Approval Date: 3 September 2021
Submission Date: 2 July 2021
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 164
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Civil and Environmental Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: PFAS, Toxicokinetic Model, Molecular Dynamics Simulation, Machine Learning, QSAR
Date Deposited: 03 Sep 2021 15:46
Last Modified: 03 Sep 2021 15:46


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item