Grantee Research Project Results
2023 Progress Report: Integrating tissue chips, rapid untargeted analytical methods and molecular modeling for toxicokinetic screening of chemicals, their metabolites and mixtures
EPA Grant Number: R840032Title: Integrating tissue chips, rapid untargeted analytical methods and molecular modeling for toxicokinetic screening of chemicals, their metabolites and mixtures
Investigators: Rusyn, Ivan , Baker, Erin D. , Chiu, Weihsueh A
Institution: Texas A & M University , North Carolina State University
EPA Project Officer: Spatz, Kyle
Project Period: August 1, 2020 through May 2, 2025
Project Period Covered by this Report: August 1, 2022 through July 31,2023
Project Amount: $799,993
RFA: Advancing Toxicokinetics for Efficient and Robust Chemical Evaluations (2019) RFA Text | Recipients Lists
Research Category: Chemical Safety for Sustainability
Objective:
The long-term objective of this project is to support risk-based decisions with new approach methodologies (NAMs) data by demonstrating integration of new biological (i.e., tissue chips), analytical (i.e., IMS-MS), and modeling methods for high fidelity, data-driven IVIVE. Specifically, this project aims to use tissue chips that are already commercially available to generate physiologically relevant toxicokinetic data, as well as to demonstrate how high-throughput detection, identification, and quantification of multiple chemicals, their metabolites, and mixtures can be achieved through the use of Ion mobility spectrometry-mass spectrometry and a computational pipeline and database of thousands of environmental chemicals. We are extending traditional IVIVE approaches by incorporating both tissue chip-based toxicokinetic data and IMS-MS analytical data into high throughput PBPK modeling of both parent chemicals and metabolites. This work will enable a more comprehensive characterization of toxicokinetics and metabolite formation, thereby reducing the uncertainty of traditional IVIVE approaches and improving the basis for decision-making with NAMs data.
The main outcome of this project will be a novel approach and a framework to support risk-based decisions on environmental chemicals and mixtures by demonstrating how an integrated set of novel biological assays (i.e., tissue chips), analytical techniques (i.e., IMS-MS) and computational methods can be used to enable high-fidelity, data-driven toxicokinetic modeling. Overall, the project is pursuing the following specific aims.
Aim 1: To generate physiologically relevant toxicokinetic data for IVIVE by using tissue chips.
Aim 2: To integrate untargeted analytical methods and metabolite predictions into IVIVE.
Aim 3: To integrate tissue chip assays and IMS-MS into high throughput PBPK models for more efficient and robust IVIVE.
Progress Summary:
The microphysiological systems (MPS), also commonly referred to as “tissue chips,” are alternatives to animal and human studies that are widely acknowledged as potentially useful for addressing particular challenges in toxicology (Rusyn et al, 2022). This program aims to demonstrate the use of MPS that are commercially available (i.e., accessible to the wider community) to generate physiologically-relevant toxicokinetic data for regulatory science applications and especially for IVIVE modeling. In Year 3, the following work was performed.
First, we continued testing various liver MPS models because of the importance of this organ to toxicokinetics. We conducted analysis of reproducibility and robustness of OrganoPlate® 2-lane 96, a liver microphysiological system manufactured by Mimetas BV (Leiden, Netherlands) for studies of pharmacokinetics and toxicological assessment of chemicals and drugs (Kato et al., 2022). Overall, we found that the OrganoPlate® 2-lane 96 device, when used with iHeps and non-parenchymal cells, is a functional liver microphysiological model; however, the high-throughput nature of this model is somewhat dampened by the need for replicates to compensate for high variability. We also conducted comparative evaluation of metabolism of a defined pesticide mixture using several in vitro liver models (Valdiviezo et al., 2022a). This study illustrated that nontarget analytical methods for detection of parent substances and their metabolites, work performed in collaboration with Drs. Baker and Chiu, can be used to characterize in vitro metabolite formation in these in vitro models following exposure to mixtures of environmental contaminants. In addition, we used several Human, Rat, and Mouse liver in vitro models, including an MPS device, as a case study of reanalysis of trichloroethylene (TCE) and tetrachloroethylene (PCE) metabolism to glutathione conjugates to improve precision in risk characterization (Valdiviezo et al., 2022b). We showed that data derived from MPCC were most consistent with estimates from physiologically based pharmacokinetic models calibrated to in vivo data. Overall, MPCC-derived data provided physiologically relevant estimates of GSH-mediated metabolism of TCE and PCE to reduce uncertainties in interspecies extrapolation that constrained previous risk evaluations, thereby increasing the precision of risk characterizations of these high-priority toxicants.
We also worked on developing dosing methods to enable cell-based in vitro testing of complex substances using a case study of a PAH mixture (Cordova et al., 2022). Specifically, we compared passive dosing via silicone micro-O-rings, cell culture media-accommodated fraction, and traditional solvent (dimethyl sulfoxide) extraction procedures. Of the tested dosing methods, media accommodated fraction (MAF) was determined to be the most appropriate method for cell-based studies of PAH-containing complex substances and mixtures. We observed that the highest fraction of the starting materials can be delivered using media accommodated fraction approach into cell culture media and thus enable concentration-response in vitro testing.
Our work on evaluating novel molecules and mixtures with ion mobility spectrometry and mass spectrometry (IMS-MS) was focused on method optimization and developing computational workflows for feature finding, molecular identification, and significance assessment for metabolites and their constituents of mixtures in environmental and experimental samples. We also applied these methods to explore the fate of dissolved organic compounds in landfill leachate and wastewater treatment systems (Doyle et al 2022) and assessed transformation products for metabolites (Bilbao et al. 2023). Great emphasis has also been placed on computational approaches to annotate IMS-MS spectra (Bilbao et al. 2023), while also removing false positive and calculating theoretical collision cross sections of higher accuracy (Rainey et al. 2022). The tools developed in these studies have all been released open-source to the scientific community, which is of extreme importance to increase capabilities of others.
To integrate tissue chip assays and IMS-MS into high-throughput PBPK models for IVIVE, we have completed model development on the Absorption portion of the PBPK model and began development of the Renal Excretion portion of the PBPK model. To this effect, we have adapted an open-source version of the ACAT model for pharmaceuticals to environmental chemicals by “turning off” drug-related components related to the dissolution-precipitation processes, thus assuming that environmental chemical is ingested in dissolved form and adding liver and kidney compartments while also incorporating probabilistic in vitro-to-in vivo extrapolation. Together, we denote this the Probabilistic Environmental Compartmental And Transit (PECAT) model (Lin and Chiu, 2023). We also have been developing a hybrid in vitro-in silico model for predicting renal clearance for compounds that undergo passive as well as active transport in the kidney (Lin et al., in preparation). In particular, utilizing human renal proximal tubule cell lines, we seeded transwells under both static and fluidic conditions (“chips”), measuring the transport of three PFAS in both directions.
Future Activities:
In Specific Aim 1, we will continue experiments with the tissue chip models for kidney, gut (Caco-2 cells and human enteroids), and liver (different commercial platforms). We will be testing a number of chemicals in each model and conduct both in vitro and analytical chemistry experiments to collect data for IVIVE analyses in Aim 3. We will be testing both individual chemicals, their metabolites, and mixtures.
In Specific Aim 2, we will complete publication of the AMP score paper. We will also continue to optimize the computational workflows using cheminformatics and machine learning with emphasis on the improving the features and utilizing the molecular descriptors determined from the IMS-MS measurements. Both parent chemicals and their transformation products will continue to be evaluated from complex biological samples and tissue chip models, and larger databases will continue to be created with the IMS-MS platform allowing improved training sets for machine learning. Errors between experimental and theoretical analyses will be assessed by evaluating molecules present in mixtures and differences in predicted theoretical collision cross section values. These will allow an understanding of how well the computations are reproducing the experimental conditions.
In Specific Aim 3, will complete publication of the in vitro-in silico kidney model for renal clearance. For absorption, we will work with Aims 1 and 2 to incorporate data from both traditional Caco-2 experiments and “gut chips” into the model to make predictions as to gut absorption. For hepatic clearance, we will continue to support Aim 1 in make clearance calculations using data from various platforms. In all cases, we will continue to utilize the literature data we have collected on ADME for comparison and analyses of accuracy.
Journal Articles on this Report : 11 Displayed | Download in RIS Format
| Other project views: | All 40 publications | 31 publications in selected types | All 31 journal articles |
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Baker E, Hoang C, Uritboonthai W, Heyman H, Pratt B, MacCross M, MacLean B, Plumb R, Aisporna A, Siuzdak G. METLIN-CCS:an ion mobility spectrometry collision cross section database. NATURE METHODS 2023;1-2. |
R840032 (2023) |
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Boatman A, Chappel J, Polera M, Dodds J, Belcher S, Baker E. Assessing Per- and Polyfluoroalkyl Substances in Fish Fillet Using Non-Targeted Analyses. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; |
R840032 (2023) |
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Chappel J, King M, Fleming J, Eberlin L, Reif D, Baker E. Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. ANALYTICAL CHEMISTRY 2023;Online ahead of print |
R840032 (2023) |
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Chappel J, Kirkwood-Donelson K, Reif D, Baker E. From big data to big insights: statistical and bioinformatic approaches for exploring the lipidome. ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2023; |
R840032 (2023) |
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Chappel J, Kirkwood-Donelson K, Dodds J, Fleming J, Reif D, Baker E. Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data. ANALYTICAL CHEMISTRY 2024; |
R840032 (2023) |
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Dodds J, Kirkwood-Donelson K, Boatman A, Knappe D, Hall N, Schnetzer A, Baker E. Evaluating Solid Phase Adsorption Toxin Tracking (SPATT) for passive monitoring of per-and polyfluoroalkyl substances (PFAS) with Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS). SCIENCE OF THE TOTAL ENVIRONMENT 2024;947. |
R840032 (2023) |
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Lin H, Chiu W. Development of a Physiologically Based Gut Absorption Model for Probabilistic Prediction of Environmental Chemical Bioavailability. ALTEX-ALTERNATIVES TO ANIMAL EXPERIMENTATION 2023;40(3):471-484 |
R840032 (2023) |
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Mirfazaelian A, Kim K-B, Anand SS, Kim HJ, Tornero-Velez R, Bruckner JV, Fisher JW. Development of a physiologically based pharmacokinetic model for deltamethrin in the adult male Sprague-Dawley rat. Toxicological Sciences 2006;93(2):432-442. |
R840032 (2023) R830800 (2005) R830800 (2006) R830800 (Final) |
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Odenkirk M, Zheng X, Kyle J, Stratton K, Niorca C, Bloodsworth K, Mclean C, Masters C, Monroe M, Doecke J, Smith R, Burnum-Johnson K, Roberts B, Baker E. Deciphering ApoE Genotype-Driven Proteomic and Lipidomic Alterations in Alzheimer's Disease Across Distinct Brain Regions. JOURNAL OF PROTEOME RESEARCH 2024; |
R840032 (2023) |
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Solosky A, Claudio I, Chapel J, Kirkwood-Donelson K, Janech M, Bland A, Gulland F, Neely B, Baker E. Proteomic and Lipidomic Plasma Evaluations Reveal Biomarkers for Domoic Acid Toxicosis in California Sea Lions. JOURNAL OF PROTEOME RESEARCH 2024;23(12):5577-5585 |
R840032 (2023) |
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Stewart AK, Foley MH, Doughetry MK,. McGill, SK, Gulati AS, Gentry EC, Hagey LR, Dorrenstein PC, Theirot CM, Dodds JN, Baker ES. Using Multidimensional Separations to Distinguish Isomeric Amino Acid-Bile Acid Conjugates and Assess Their Presence and Perturbations in Model Systems.ANALYTICAL CHEMISTRY 2023;95(41):15357-15366. |
R840032 (2023) |
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Supplemental Keywords:
in vitro-to-in vivo, tissue chips, rapid analytical methods, IMS-MS, NAMsRelevant Websites:
NCSU Collision Cross Section Database Exit
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.