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用于药物发现与开发的芯片肝脏模型

Liver-on-chips for drug discovery and development.

作者信息

Mehta Viraj, Karnam Guruswamy, Madgula Vamsi

机构信息

Organoid Technology Lab, DMPK Department, Sai Life Sciences, Hyderabad, 500078, India.

出版信息

Mater Today Bio. 2024 Jul 2;27:101143. doi: 10.1016/j.mtbio.2024.101143. eCollection 2024 Aug.

DOI:10.1016/j.mtbio.2024.101143
PMID:39070097
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11279310/
Abstract

Recent FDA modernization act 2.0 has led to increasing industrial R&D investment in advanced 3D models such as organoids, spheroids, organ-on-chips, 3D bioprinting, and approaches. Liver-related advanced models remain the prime area of interest, as liver plays a central role in drug clearance of compounds. Growing evidence indicates the importance of recapitulating the overall liver microenvironment to enhance hepatocyte maturity and culture longevity using (LoC) . Hence, pharmaceutical industries have started exploring LoC assays in the two of the most challenging areas: accurate - extrapolation (IVIVE) of hepatic drug clearance and drug-induced liver injury. We examine the joint efforts of commercial chip manufacturers and pharmaceutical companies to present an up-to-date overview of the adoption of LoC technology in the drug discovery. Further, several roadblocks are identified to the rapid adoption of LoC assays in the current drug development framework. Finally, we discuss some of the underexplored application areas of LoC models, where conventional 2D hepatic models are deemed unsuitable. These include clearance prediction of metabolically stable compounds, immune-mediated drug-induced liver injury (DILI) predictions, bioavailability prediction with gut-liver systems, hepatic clearance prediction of drugs given during pregnancy, and dose adjustment studies in disease conditions. We conclude the review by discussing the importance of PBPK modeling with LoC, digital twins, and AI/ML integration with LoC.

摘要

近期的美国食品药品监督管理局(FDA)现代化法案2.0促使工业界对类器官、球状体、芯片器官、3D生物打印等先进3D模型以及相关方法的研发投资不断增加。与肝脏相关的先进模型仍是主要关注领域,因为肝脏在化合物的药物清除过程中起着核心作用。越来越多的证据表明,利用肝脏类器官芯片(LoC)概括整个肝脏微环境对于提高肝细胞成熟度和培养寿命至关重要。因此,制药行业已开始在两个最具挑战性的领域探索LoC检测方法:肝脏药物清除的准确体外到体内外推(IVIVE)和药物性肝损伤。我们研究了商业芯片制造商和制药公司的共同努力,以呈现药物研发中LoC技术应用的最新概况。此外,还确定了在当前药物开发框架中快速采用LoC检测方法的几个障碍。最后,我们讨论了一些LoC模型尚未充分探索的应用领域,在这些领域中传统的二维肝脏模型被认为不合适。这些领域包括代谢稳定化合物的清除预测、免疫介导的药物性肝损伤(DILI)预测、肠 - 肝系统的生物利用度预测、孕期用药的肝脏清除预测以及疾病状态下的剂量调整研究。我们通过讨论LoC的生理药代动力学(PBPK)建模、数字孪生以及LoC与人工智能/机器学习集成的重要性来结束本综述。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/defb7962a64a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/46a5d94c2e33/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/d2120507e56a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/bf55a0ca6d37/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/8247b330afe1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/defb7962a64a/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/46a5d94c2e33/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/d2120507e56a/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/bf55a0ca6d37/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/8247b330afe1/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a35/11279310/defb7962a64a/gr4.jpg

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