Grantee Research Project Results

Final Report: Integrating tissue chips, rapid untargeted analytical methods and molecular modeling for toxicokinetic screening of chemicals, their metabolites and mixtures

EPA Grant Number: R840032
Title: Integrating tissue chips, rapid untargeted analytical methods and molecular modeling for toxicokinetic screening of chemicals, their metabolites and mixtures
Investigators:
Institution:
EPA Project Officer:
Project Period: August 1, 2020 through May 2, 2025
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 was 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 aimed 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 were 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 project goals focused on the main outcome – to develop 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 was 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.

Conclusions:

Aim 1 focused on creating and validating robust, reproducible microphysiological systems (MPS, colloquially “organ-on-chip” platforms) that can accurately model human tissue-level responses to toxicant exposure. A primary objective was to overcome current limitations related to throughput, inter-laboratory reproducibility, and physiological relevance. Major outputs and outcomes included:

MPS Platform Development and Testing/Qualification

• Creation of Modular Tissue Chip-Based Systems:
  The center tested modular liver, kidney, and lung chips compatible with 96-well plate formats. Each platform incorporated key cell types (e.g., hepatocytes, renal epithelial cells, alveolar epithelial cells, and relevant stromal cells), extracellular matrix components, and supports perfusion to mimic physiological conditions.
• Standardization & Reproducibility:
  Standardized protocols for chip operation, cell seeding, and media perfusion were established and validated within consortium laboratories. Inter-lab reproducibility studies showed high concordance in baseline functionality and response to reference compounds.

Biological Validation of Benchmark Functionalities:
Each chip platform met or exceeded established benchmarks for tissue-specific functions:

• • Liver chips: albumin and urea production, CYP enzyme activity
  • Kidney chips: filtration/barrier integrity (measured via TEER and marker dye leakage)
  • Lung chip: barrier integrity, surfactant production, cilia motility

Reference Compound Testing:
Standard environmental toxicants (e.g., acetaminophen, cisplatin, paraquat) were used to verify that chips recapitulate known dose-response behaviors, including target organ-specific toxicity phenotypes. Raw data (viability, biomarker output, metabolic profiling) and detailed SOPs were curated and made available to the research community, facilitating method adoption and meta-analyses.

Key Takeaways and Impact of Aim 1: The deliverables of Aim 1 provide a validated, scalable, and reproducible foundation for organ-specific hazard assessment. These platforms are now being distributed to collaborators within the EPA’s ToxCast and HTTr (High Throughput Transcriptomics) networks, streamlining incorporation into regulatory science.

Aim 2 addressed the need for high-content and multiplexed analytical modalities suitable for the medium-to-high-throughput chip platforms developed in Aim 1. The goal was to maximize information yield per sample/experiment and speed up hazard identification. Major outputs and outcomes included:

Analytical Platform Innovation

• Implementation of exposomics:
  Integrated protocols enable simultaneous profiling of multiple chemicals and metabolites directly from chip effluents.
  • High-content imaging (live/dead staining, immunofluorescence for mechanistic biomarkers, morphological profiling) was automated and multiplexed for batch analytics.
• Development of Automated Workflows:
  Automated liquid handling and parallelized sample processing pipelines expedited sample preparation and data acquisition, reducing hands-on time and variability.

Data Integration

• Cross-Modality Data Analysis Pipelines:
  Developed cloud-based bioinformatics pipelines to integrate omics with chemical exposure data and phenotypic outcomes. We established new approaches for pathway-level and adverse outcome pathway (AOP) mapping, linking in vitro assay signatures to likely in vivo effects.

Outputs and Data sharing

• All custom scripts, data standards, and hardware control protocols are published open-access, greatly expanding the toolset available to the wider toxicology and risk assessment community.

Key Takeaways and Impact of Aim 2: The integration of multiplexed analytics with standardized MPS platforms elevated the granularity and throughput of toxicity assessment. The center’s workflows are being incorporated into national research consortia and public-private partnerships, supporting regulatory acceptance of non-animal hazard assessment approaches.

Aim 3 translated the validated platforms and analytics into public health–relevant case studies, with an emphasis on priority environmental contaminants identified by the EPA and other federal partners. Major outputs and outcomes included:

Case Study Selection and Execution

• Chemical Prioritization:
  Chemical libraries included well-studied toxicants (for benchmarking) and ~50 emerging contaminants with limited human hazard data (e.g., PFAS, pesticides, industrial byproducts).
• Exposure Scenario Modeling:
  Designed exposure regimens (acute vs. chronic; single vs. mixtures) relevant to real-world scenarios informed by EPA monitoring data.

Discovery and Decision-Support Insights

• Identification of Chemical Hazards:
  Studies identified previously unreported toxicity and transport kinetics for selected PFAS and other chemicals.
• Comparative Toxicology:
  Chip-based responses were compared with existing animal and traditional in vitro data, demonstrating strong concordance with human-relevant endpoints, and in some cases, greater sensitivity to lower exposures.
• Mixture Effects Testing:
  The platforms successfully deconvoluted complex mixture effects, identifying additive or synergistic toxicities that would be missed by single-chemical assessments.

Policy and Research Translation

• Data for Risk Assessment:
  Study results were shared with EPA risk assessors, resulting in new data inputs for predictive models and for prioritization within the Agency.
• Community Engagement:
  Summary findings were communicated to partner agencies and stakeholder groups via public webinars, policy briefs, and co-authored white papers.

Key Takeaways and Impact of Aim 3: Application of integrated OoC–omics platforms to EPA-relevant chemicals delivered highly actionable, human-relevant hazard data. The project provided direct support to agency efforts to streamline chemical evaluations and has catalyzed further collaborations on mixture toxicity and non-animal risk assessment.

Overall Project Impact

The “Integrating Chips with Rapid Analytics” STAR Center successfully delivered on its mission to modernize and accelerate environmental toxicology. Key impacts include:

• Establishment of open, validated organ-on-chip platforms and analytics workflows,
• Demonstrated use in high-impact, policy-relevant chemical evaluations,
• Advancement of the field toward animal-free, human-centered toxicity testing as recommended by the National Academies and federal strategy documents.

All validated platforms, data resources, and protocols are now available for adoption by the wider research, regulatory, and industrial communities, positioning the STAR Center as a hub and model for next-generation toxicology.

References:

1. Rusyn, I., and Roth, A. Editorial overview of the special issue on application of tissue chips in toxicology. Toxicology 450:152687, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8720269/
2. Sakolish, C., Reese, C., Valdiviezo, A., Luo, Y.S., Schurdak, M., Gough, M., Taylor, D.L., Chiu, W.A., and Rusyn, I. Analysis of Reproducibility and Robustness of a Human Microfluidic Four-Cell Liver Acinus MicroPhysiology System (LAMPS). Toxicology 448:152651, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC7785655/
3. Odenkirk, M.T., Reif, D. M., Baker, E.S. Multi-Omic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments. Anal Chem 93:7763-7773, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8465926/
4. Dodds, J.N., Alexander, N.L.M., Kirkwood, K.I., Foster, M. R., Hopkins, Z.R., Knappe, D.R.U., Baker E.S., From Pesticides to Per- and Polyfluoroalkyl Substances: An Evaluation of Recent Targeted and Untargeted Mass Spectrometry Methods for Xenobiotics. Anal Chem 93:641-656, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC7855838/
5. Hsieh NH, Bois FY, Tsakalozou E, Ni ZL, Yoon M, Sun W, Klein M, Reisfeld B, Chiu WA. A Bayesian Population Physiologically Based Pharmacokinetic Absorption Modeling Approach to Support Generic Drug Development: Application to Bupropion Hydrochloride Oral Dosage Forms. J Pharmacokinet Pharmacodynam 48(6):893-908, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8604781/
6. Roman-Hubers, A.T., Cordova, A., Aly, N.A., McDonald, T.J., Lloyd, D., Wright, F.A., Baker, E.S., Chiu, W.A., and Rusyn, I. A data processing workflow to identify structurally related compounds in petroleum substances using ion mobility spectrometry-mass spectrometry. Energ Fuel 35(13):10529-10539, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8341389/
7. Valdiviezo, A., Luo, Y.S., Chen, Z., Chiu, W.A., and Rusyn, I. Quantitative in vitro-to-in vivo extrapolation for mixtures: A case study of Superfund priority list pesticides. Toxicol Sci 183(1):60-69, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8404988/
8. Kim, S., Richardson, L., Radnaa, E., Chen, Z., Rusyn, I., Menon, R., and Han, A. Molecular Mechanisms of Environmental Toxin Cadmium at the Feto-Maternal Interface Investigated Using an Organ-On-Chip (FMi-OOC) Model. J Hazard Mater 422:126759, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8595660/
9. Sakolish, C., Luo, Y.S., Valdiviezo, A., Vernetti, L.A., Rusyn, I. and Chiu, W.A. Prediction of Hepatic Drug Clearance with a Human Microfluidic Four-Cell Liver Acinus MicroPhysiology System. Toxicology 463:152954, 2021. https://pmc.ncbi.nlm.nih.gov/articles/PMC8585690/
10. Valdiviezo, A., Aly, N.A., Luo, Y.S., Cordova, A., Casillas, G., Foster, M., Baker, E.S., and Rusyn, I. Analysis of per- and polyfluoroalkyl substances in Houston Ship Channel and Galveston Bay following a large-scale fire using ion-mobility-spectrometry-mass spectrometry. J Environ Sci 115:350-362, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8724578/
11. Kirkwood, K. I., Christopher, M. W., Burgess, J. L., Littau, S. R., Foster, K., Richey, K., Pratt, B. S., Shulman, N., Tamura, K., MacCoss, M. J., MacLean, B. X., Baker, E. S. Development and Application of Multidimensional Lipid Spectral Libraries to Investigate Lipidomic Dysregulation Related to Smoke Inhalation Injury Severity. J. Proteome Res. 21:232-242, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8741653/
12. Aly, N.A., Dodds, J.N., Luo, Y.S., Grimm, F.A., Foster, M., Rusyn, I., and Baker, E.S. Utilizing ion mobility spectrometry-mass spectrometry for the characterization and detection of persistent organic pollutants and their metabolites. Anal Bioanal Chem 414(3):1245-1258, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8727508/
13. Bilbao, A., Gibbons, B. C., Stow, S., Kyle, J. E., Bloodsworth, K. J., Payne, S. H., Smith, R. D., Ibrahim, Y. M., Baker, E. S., Fjeldsted, J. C. A Preprocessing Tool for Enhanced Ion Mobility-Mass Spectrometry-Based Omics Workflows. J. Proteome Res. 21:798-807, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8837709/
14. Odenkirk, M. T., Horman, B. M., Dodds, J. N., Patisaul, H. B., Baker, E. S., Combining Micropunch Histology and Multidimensional Lipidomic Measurements for In-Depth Tissue Mapping. ACS Meas. Sci. Au. 2:67-75, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9139744/
15. Anklam, E., Bahl, M.I., Ball, R., Beger, R.D., Cohen, J., Fitzpatrick, S., Girard, P., Halamoda-Kenzaoui, B., Hinton, D., Hirose, A., Hoeveler, A., Honma, M., Hugas, M., Ishida, S., Kass, G.E., Kojima, H., Krefting, I., Liachenko, S., Liu, Y., Masters, S., Marx, U., McCarthy, T., Mercer, T., Patri, A., Pelaez, C., Pirmohamed, M., Platz, S., Ribeiro, A.J., Rodricks, J.V., Rusyn, I., Salek, R.M., Schoonjans, R., Silva, P., Svendsen, C.N., Sumner, S., Sung, K., Tagle, D., Tong, L., Tong, W., Eijnden-van-Raaij, J.V.D., Vary, N., Wang, T., Waterton, J., Wang, M., Wen, H., Wishart, D., Yuan, Y., and Slikker, W. Jr. Emerging technologies and their impact on regulatory science. Exp Biol Med 247(1):1-75, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8749227/
16. Roman-Hubers, A.T., Cordova, A.C., Rohde, A.M., Chiu, W.A., McDonald, T.J., Wright, F.A., Dodds, J.N., Baker, E.S., and Rusyn, I. Characterization of compositional variability in petroleum substances. Fuel 317:123547, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8896784/
17. Dodds, J. N., Wang, L., Patti, G., Baker, E. S. Combining Isotopologue Workflows and Simultaneous Multidimensional Separations to Detect, Identify and Validate Metabolites in Untargeted Analyses. Anal. Chem. 94:2527-2535, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC8934380/
18. Sakolish, C., Georgescu, A., Huh, D.D., and Rusyn, I. A model of human small airway on a chip for studies of sub-acute effects of inhalation toxicants. Toxicol Sci 187(2):267-278, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9154286/
19. Rusyn, I. and Chiu, W.A. Decision-Making with New Approach Methodologies: Time to Replace Default Uncertainty Factors with Data. Toxicol Sci. 189:148-149, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9801706/
20. Rusyn, I., Sakolish, C., Kato, Y., Stephan, C., Vergara, L., Hewitt, P., Bhaskaran, V., Davis, M., Hardwick, R., Ferguson, S.S., Stanko, J.P., Bajaj, P., Adkins, K., Sipes, N.S., Hunter, S., Baltazar, M.T., Carmichael, P.L., Sadh. K., and Becker, R.A. Microphysiological Systems Evaluation: Experience of TEX-VAL Tissue Chip Testing Consortium. Toxicol Sci. 188:143-152, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9333404/
21. Foster, M. R., Rainey, M., Watson, C. A., Dodds, J. N., Kirkwood, K. I., Fernandez, F. M., Baker, E. S. Uncovering Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect and Machine Learning. Environ. Sci. Technol. 56(12):9133-9143, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9474714/
22. Kirkwood, K. I., Pratt, B. S., Shulman, N., Tamura, K., MacCoss, M. J., MacLean, B. X., Baker, E. S. Utilizing Skyline to Analyze Lipidomics Data Containing Lipid Chromatography, Ion Mobility Spectrometry and Mass Spectrometry Dimensions. Nat. Protoc. 17(11):2415-2430, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9633456/
23. Valdiviezo, A., Brown, G.E., Michell, A.R., Trinconi, C.M., Bodke, V.V., Khetani, S.R., Luo, Y.S., Chiu, W.A., and Rusyn I. Reanalysis of trichloroethylene and tetrachloroethylene metabolism to glutathione conjugates using human, rat, and mouse liver in vitro models to improve precision in risk characterization. Environ Health Perspect 130(11):117009, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9707501/
24. Valdiviezo, A., Kato, Y., Baker, E.S., Chiu, W.A., and Rusyn, I. Evaluation of Metabolism of a Defined Pesticide Mixture through Multiple In Vitro Liver Models. Toxics 10(10):566, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9609317/
25. Kato, Y., Lim, A.Y., Sakolish, C., Valdiviezo, A., Moyer, L.H., Hewitt, P., Bajaj, P., Han, G., Rusyn, I. Analysis of reproducibility and robustness of OrganoPlate® 2-lane 96, a liver microphysiological system for studies of pharmacokinetics and toxicological assessment of drugs. Toxicol In Vitro 85:105464, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC10015056/
26. Doyle, M.G., Odenkirk, M.T., Stewart, A.K., Nelson, J.P., Baker, E.S., De La Cruz, F. Assessing the Fate of Dissolved Organic Compounds in Landfill Leachate and Wastewater Treatment Systems. ACS ES&T Water 2(12):2502, 2022. https://pubmed.ncbi.nlm.nih.gov/36911356/
27. Rainey, M.A., Watson, C.A., Asef, C.K., Foster, M.R., Baker, E.S., Fernández, F.M. CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics. Anal. Chem. 94(50):17456, 2022. https://pmc.ncbi.nlm.nih.gov/articles/PMC9772062/
28. Cordova, A.C., Ford, L.C., Valdiviezo, A., Roman-Hubers, A.T., McDonald, T.J., Chiu, W.A., and Rusyn, I. Dosing methods to enable cell-based in vitro testing of complex substances: A case study with a PAH mixture. Toxics 11(1):19, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC9866728/
29. Bilbao, A., Munoz, N., Kim, J., Orton, D.J., Gao, Y., Poorey, K., Pomraning, K., Weitz, K., Burnet, M., Nicora, C.D., Wilton, R., Deng, S., Dai, Z., Oksen, E., Gee, A., Fasani, F.A, Tsalenko, A., Tanjore, D., Gardner, J.,Smith, R.D., Michener, J.K., Gladden, J.M., Baker, E.S., Petzold, C.J., Kim, Y-M. Apffel, A., Magnuson, J.K., Burnum-Johnson, K.E. PeakDecoder Enables Machine Learning-based Metabolite Annotation and Accurate Profiling in Multidimensional Mass Spectrometry Measurements. Nat Commun 14:2461, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10147702/
30. Lin, H.C., Chiu W.A. Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability. ALTEX. 40(3):471-484, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10898273/
31. Sakolish, C., Moyer, H.L., Tsai, H.D., Ford, L.C., Dickey, A.N., Wright, F.A., Han, G., Bajaj, P., Baltazar, M.T., Carmichael, P.L., Stanko, J.P., Ferguson, S.S., and Rusyn, I. Analysis of Reproducibility and Robustness of a Renal Proximal Tubule Microphysiological System OrganoPlate 3-lane 40 for In Vitro Studies of Drug Transport and Toxicity. Toxicol Sci 196(1):52-70, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10613961/
32. Lim, A.Y., Kato, Y., Sakolish, C., Valdiviezo, A., Han, G., Bajaj, P., Stanko, J., Ferguson, S.S., Villenave, R., Hewitt, P., Hardwick, R.N., and Rusyn, I. Reproducibility and Robustness of a Liver Microphysiological System PhysioMimix LC12 under Varying Culture Conditions and Cell Type Combinations. Bioengineering 10(10):1195, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10603899/
33. Kirkwood-Donelson, K.I., Dodds, J.N., Schnetzer, A., Hall, N., Baker, E.S. Uncovering Per- and Polyfluoroalkyl Substances (PFAS) with Nontargeted Ion Mobility Spectrometry-Mass Spectrometry Analyses. Sci Adv 9(43):eadj7048, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10599621/
34. Chappel, J.R., King, M.E., Fleming, J., Eberlin, L.S., Reif, D.M., Baker, E.S.  Aggregated Molecular Phenotype Scores: Enhancing Assessment and Visualization of Mass Spectrometry Imaging Data for Tissue-Based Diagnostics. Analytical Chemistry 95(34):12913-12922, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10561690/
35. Stewart, A.K., Foley, M.H., Dougherty, M.K., McGill, S.K., Gulati, A.S., Gentry, E.C., Hagey, L.R., Dorrestein, P.C., Theriot, C.M., Dodds, J.N., Baker, E.S. Using Multidimensional Separation to Distinguish Isomeric Bile Acid-Amino Acid Conjugates and Assess Their Presence and Perturbations. Anal Chem 95(41):15357-15366, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10613829/
36. Baker, E.S., Hoang, C., Uritboonthai, W., Heyman, H.M., Pratt, B., MacCoss, M., MacLean, B., Plumb, R., Aisporna, A., Siuzdak, G. METLIN-CCS: An Ion Mobility Spectrometry Collision Cross Section Database. Nat Methods 20(12):1836-1837, 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10843661/
37. Chappel, J.R., Kirkwood-Donelson, K.I., Reif, D.M., Baker, E.S. From Big Data to Big Insights: Statistical and Bioinformatic Approaches for Exploring the Lipidome. Anal Bioanal Chem 416(9):2189-2202, 2024.  https://pmc.ncbi.nlm.nih.gov/articles/PMC10954412/
38. Vidal, M.S., Richardson, L.S., Kammala, A.K., Kim, S., Lam, P.Y., Cherukuri, R., Thomas, T.J., Bettayeb, M., Han, A., Rusyn, I., and Menon, R. Endocrine-disrupting compounds and their impact on human placental function: Evidence from placenta organ- on-chip studies. Lab Chip 24(6):1727-1749, 2024. https://pubmed.ncbi.nlm.nih.gov/38334486/
39. Lin, H.C., Sakolish, C., Moyer, H.L., Carmichael, P.L., Baltazar, M.T., Ferguson, S.S., Stanko, J.P., Hewitt, P., Rusyn, I., and Chiu, W.A. An in vitro-in silico workflow for predicting renal clearance of PFAS. Toxicol Appl Pharmacol 23;489:117015, 2024. https://www.sciencedirect.com/science/article/abs/pii/S0041008X24002138?via%3Dihub
40. Kirkwood-Donelson, K.I., Chappel, J., Tobin, E., Dodds, J.N., Reif, D.M., DeWitt, J.C., Baker, E.S. Investigating Mouse Hepatic Lipidome Dysregulation Following Exposure to Emerging Per- and Polyfluoroalkyl Substances (PFAS). Chemosphere 354:141654, 2024. https://www.sciencedirect.com/science/article/abs/pii/S0045653524005472?via%3Dihub
41. Baker, E.S., Uritboonthai, W., Aisporna, A., Hoang, C., Heyman, H.M., Giera, M., Siuzdak, G. METLIN-CCS Lipid Database: An Authentic Standards Resource for Lipid Classification and Identification. Nat Metabol 6:981-982, 2024. https://pmc.ncbi.nlm.nih.gov/articles/PMC11218851/
42. Solosky, A.M., Kirkwood-Donelson, K.I., Odenkirk, M.T., Baker, E.S. Recent Additions and Access to a Multidimensional Lipidomic Database Containing Liquid Chromatography, Ion Mobility Spectrometry and Tandem Mass Spectrometry Information. Anal Bioanal Chem 416(25):5423-5429. https://pmc.ncbi.nlm.nih.gov/articles/PMC11427178/
43. Sakolish, C., Moyer, H.L., Tsai, H.D., Ford, L.C., Dickey, A.N., Bajaj, P., Villenave, R., Hewitt, P., Ferguson, S.S., Stanko, J., and Rusyn, I. Comparative Analysis of the Physiological and Transport Functions of Various Sources of Renal Proximal Tubule Cells Under Static and Fluidic Conditions in PhysioMimix T12 Platform. Drug Metab Dispos 53(1):100001. https://dmd.aspetjournals.org/content/early/2024/04/16/dmd.124.001488

Journal Articles:

No journal articles submitted with this report: View all 40 publications for this project

Supplemental Keywords:

in vitro-to-in vivo, tissue chips, rapid analytical methods, IMS-MS, NAMs.

Relevant Websites:

The source code of "Hsieh NH, Bois FY, Tsakalozou E, Ni Z, Yoon M, Sun W, Klein M, Reisfeld B, Chiu WA. A Bayesian population physiologically based pharmacokinetic absorption modeling approach to support generic drug development: application to bupropion hydrochloride oral dosage forms. Journal of Pharmacokinetics and Pharmacodynamics 2021 Sep; 22:1-6" Exit

Progress and Final Reports:

Original Abstract

2021 Progress Report

2022 Progress Report

2023 Progress Report

2024 Progress Report