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A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues

Cite this: Chem. Res. Toxicol. 2020, 33, 1, 258–270
Publication Date (Web):December 10, 2019
Copyright © 2019 American Chemical Society

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    Abstract Image

    The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The “omics”, such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins.

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    • Optimization of the size and number of the MTs. Table S1: Description of different-sized microtissues and their reproducibility. Table S2: Characterization of MT for each set of analyses. Table S3: Composition of the lipid fingerprint (282 lipid species). Table S4: Lipidomics signature risk profile based on the length of the vectors. Figure S1: Lipid extraction reproducibility in different-sized microtissues. Figure S2: Microscope images of 3D Insight human liver microtissues. Figure S3: Experimental design for two MT plates. Figure S4: Example of chromatograms (PDF)

    • Molecular formula strings, predicted toxicity level (XLSX)

    • Diameters of 3D liver microtissues (XLSX)

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    Cited By

    This article is cited by 9 publications.

    1. Xiao-yan Wu, Li-juan Xie, Jun-jie He, Xing-xu Yan, Fang-fang Zhang, Yan-yan Xu, Yu-bo Li. Lipidomics reveals the lipid metabolism disorders in Fructus Psoraleae-induced hepatotoxicity in rats with kidney-yin deficiency syndrome. Journal of Chromatography B 2023, 1229 , 123898.
    2. Guillermo Quintás, José V. Castell, Marta Moreno-Torres. The assessment of the potential hepatotoxicity of new drugs by in vitro metabolomics. Frontiers in Pharmacology 2023, 14
    3. Limei Li, Qingce Zang, Xinzhu Li, Ying Zhu, Shanjing Wen, Jiuming He, Ruiping Zhang, Zeper Abliz. Spatiotemporal pharmacometabolomics based on ambient mass spectrometry imaging to evaluate the metabolism and hepatotoxicity of amiodarone in HepG2 spheroids. Journal of Pharmaceutical Analysis 2023, 13 (5) , 483-493.
    4. Nguyen Hoang Anh, Young Cheol Yoon, Young Jin Min, Nguyen Phuoc Long, Cheol Woon Jung, Sun Jo Kim, Suk Won Kim, Eun Goo Lee, Daijie Wang, Xiao Wang, Sung Won Kwon. Caenorhabditis elegans deep lipidome profiling by using integrative mass spectrometry acquisitions reveals significantly altered lipid networks. Journal of Pharmaceutical Analysis 2022, 12 (5) , 743-754.
    5. Thomas Kralj, Kim L R Brouwer, Darren J Creek. Analytical and Omics-Based Advances in the Study of Drug-Induced Liver Injury. Toxicological Sciences 2021, 183 (1) , 1-13.
    6. Joyita Sarkar, Ashok Kumar. Recent Advances in Biomaterial‐Based High‐Throughput Platforms. Biotechnology Journal 2021, 16 (2)
    7. Khaled Mohamed Mohamed Koriem. Lipidome is lipids regulator in gastrointestinal tract and it is a life collar in COVID-19: A review. World Journal of Gastroenterology 2021, 27 (1) , 37-54.
    8. Henri F. Avela, Heli Sirén. Advances in lipidomics. Clinica Chimica Acta 2020, 510 , 123-141.
    9. Henri F. Avela, Heli Sirén. Advances in analytical tools and current statistical methods used in ultra-high-performance liquid chromatography-mass spectrometry of glycero-, glycerophospho- and sphingolipids. International Journal of Mass Spectrometry 2020, 457 , 116408.

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