Grantee Research Project Results
2021 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 July 30, 2023 (Extended to July 30, 2024)
Project Period Covered by this Report: August 1, 2020 through July 31,2021
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:
Even though the microphysiological systems (MPS), also commonly referred to as “tissue chips,” are new technologies that begin to contribute to advances in basic and mechanistic knowledge in a number of biomedical disciplines, few publications demonstrate how these devices can be used to solve a particular challenge in drug development or toxicology, or replace the existing animal or in vitro test (Rusyn and Roth, 2021). Therefore, we aimed to demonstrate the use of tissue chips 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. To this end, we focused our work in Year 1 on the liver.
First, we used a human microfluidic four-cell liver acinus microphysiology system (LAMPS) tissue chip and evaluated it for reproducibility and robustness as a model for drug pharmacokinetics and toxicology. In this published work (Sakolish et al., 2021), we demonstrated that LAMPS is a reproducible liver tissue chip in terms of basal function, chemical metabolism, and effects of hepatotoxicants. We showed that when seeded with either primary human hepatocytes or iPSC-derived hepatocytes, this model is superior to traditional 2D cultures. Using these data, we investigated the ability of LAMPS to make predictions as to hepatic clearance for chemicals. In this study (submitted), we aimed to obtain hepatic clearance estimates for seven representative chemicals from experiments using LAMPS or 2D cultures and compared with both in vivo clinically-derived and in vitro hepatocyte suspension culture-derived values reported in the literature. We found that, compared to in vivo clinically-derived values, the LAMPS model with iPSC-derived hepatocytes had the higher precision as compared to primary cells in suspension or 2D culture, but, consistent with previous studies in other microphysiological systems, tended to underestimate in vivo clearance. This study suggests that using LAMPS and iPSC-derived hepatocytes together with an empirical scaling factor to address systematic underprediction of in vivo metabolic activity may be a feasible approach.
Second, we sought to quantitatively characterize in vitro-to-in vivo extrapolation (IVIVE) parameters for chemicals in the mixture. We hypothesized that chemical co-exposures could modulate both protein binding efficiency and hepatocyte clearance of the chemicals in a mixture, which would in turn affect the quantitative IVIVE toxicokinetic parameters. To test this, we used 20 pesticides, both individually and as equimolar mixtures, and investigated the concentration-dependent effects of chemical interactions on in vitro toxicokinetic parameters. We found (Valdiviezo et al., 2021) that for single chemicals, the protein binding efficiencies were similar at different test concentrations. In a mixture, however, both protein binding efficiency and hepatocyte clearance were affected. When IVIVE was conducted using mixture-derived toxicokinetic data, more conservative estimates of Activity-to-Exposure Ratios were produced as compared to using data from single chemical experiments. Because humans are exposed to mixtures of chemicals, the findings in this study are significant as they demonstrate the importance of incorporating mixture-derived parameters into IVIVE for in vitro bioactivity data in order to accurately prioritize risks and facilitate science-based decision-making.
Third, we worked to tackle a number of key challenges in identification and quantification of chemicals, their metabolites, and constituents of mixtures in environmental and experimental samples. Rapid chemical analyses and molecular identifications, utilizing a combination of chemical information obtained from novel Ion Mobility Spectrometry−Mass Spectrometry (IMS-MS) analytical methods such as high-accuracy MS values, MS/MS profiles, isotopic signatures, and IMS structural information (collision cross section [CCS] values), as well as computational methods to predict these values for uncharacterized chemicals and/or metabolites (Odenkirk et al., 2021), would allow for efficient evaluation of chemicals and their metabolites in complex samples. To achieve this, we established a data processing workflow to identify structurally related compounds in complex hydrocarbon-containing substances and mixtures by using IMS-MS (Roman-Hubers et al., in press). Also, we demonstrated how IMS-MS can be used to identify persistent organic pollutants (POPs) and their metabolites. We characterized compounds from various classes of POPs and their metabolites and/or degradants using IMS-MS (submitted). We determined which ionization sources are best to be employed to ensure optimal ionization and detection for each chemical. Collectively, this study advanced the field of exposure assessment by characterizing analytical features for environmental pollutants.
Future Activities:
In Specific Aim 1, we will continue experiments with the tissue chip models for kidney (both glomerulus and proximal tubule), gut, and liver (different commercial platforms for the liver). 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. We will be testing both individual chemicals, their metabolites, and mixtures.
In Specific Aim 2, we will continue to utilize IMS-MS for the evaluation of both parent chemicals and their metabolites as standards. We will also begin evaluating the chemical mixtures resulting from the tissue chip models. The observed transformations from these analyses will then be incorporated into computational models to assess and annotate the features from the tissue chips using both experimental and predicted IMS collision cross sections and m/z values.
In Specific Aim 3, we will adapt the ACAT model for environmental chemicals so as to enable analysis of the gut chip data that will become available from Aims 1 and 2. Additionally, when kidney chip data are available, we will apply the parallel tube model to derive renal excretion parameters. In all cases, we will utilize the literature data we have collected on ADME for comparison and analyses of accuracy.
Journal Articles on this Report : 4 Displayed | Download in RIS Format
Other project views: | All 19 publications | 16 publications in selected types | All 16 journal articles |
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Sakolish C, Reese CE, Luo YS, Valdiviezo A, Schurdak ME, Gough A, Taylor DL, Chiu WA, Venetti LA, Rusyn I. Analysis of reproducibility and robustness of a human microfluidic four-cell liver acinus microphysiology system (LAMPS). Toxicology 2021;448. |
R840032 (2021) R835736 (Final) |
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Dodds JN, Alexander NL, Kirkwood KI, Foster MR, Hopkins ZR, Knappe DR, Baker ES. From Pesticides to Per-and Polyfluoroalkyl Substances:An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics. Analytical chemistry 2020;93(1):641-56. |
R840032 (2021) |
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Odenkirk MT, Reif DM, Baker ES. Multiomic Big Data Analysis Challenges:Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Analytical Chemistry 2021. |
R840032 (2021) R840032 (2022) |
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Rusyn I, Roth A. Editorial overview of the special issue on application of tissue chips in toxicology. Toxicology 2021:152687-. |
R840032 (2021) |
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Supplemental Keywords:
in vitro-to-in vivo, tissue chips, rapid analytical methods, IMS-MS, NAMs.Relevant Websites:
NC State University Exit , Git Hub 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.