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<p>During drug development, it is crucial to ensure that a drug exhibits effective activity in its target cells and organs. However, regardless of its effectiveness, a drug cannot be administered to patients if it exhibits toxicity in vivo. Based on pharmacokinetics, most drugs are cleared from the liver after entering the body, and only the remaining fraction reaches the target organ to exert therapeutic effects. Consequently, drugs with in vivo toxicity often manifest hepatotoxicity as an initial sign. This highlights the critical importance of hepatotoxicity assessment in drug development. Currently, hepatotoxicity assessments primarily rely on animal models and primary human hepatocytes. However, there are instances in which drugs pass these evaluations, are released to the market, and are later withdrawn because of unforeseen toxicity in patients. To enhance prediction accuracy, emerging hepatotoxicity models—including advanced 3D liver culture systems, in silico approaches such as AI-based models, and improved in vitro assays—are gaining significant attention. This review systematically compares conventional 2D models, animal models, organ-on-a-chip systems, and computational models, highlighting their advantages, limitations, and predictive reliability. By critically evaluating these methodologies, we propose future directions for refining hepatotoxicity assessment strategies, with an emphasis on enhancing translational relevance, reducing reliance on animal testing, and integrating AI-driven predictive models.</p>

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Advancing hepatotoxicity assessment: current advances and future directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月24日

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  • Yewon Kim,
  • Hojin Kim,
  • Yohan Kim

摘要

During drug development, it is crucial to ensure that a drug exhibits effective activity in its target cells and organs. However, regardless of its effectiveness, a drug cannot be administered to patients if it exhibits toxicity in vivo. Based on pharmacokinetics, most drugs are cleared from the liver after entering the body, and only the remaining fraction reaches the target organ to exert therapeutic effects. Consequently, drugs with in vivo toxicity often manifest hepatotoxicity as an initial sign. This highlights the critical importance of hepatotoxicity assessment in drug development. Currently, hepatotoxicity assessments primarily rely on animal models and primary human hepatocytes. However, there are instances in which drugs pass these evaluations, are released to the market, and are later withdrawn because of unforeseen toxicity in patients. To enhance prediction accuracy, emerging hepatotoxicity models—including advanced 3D liver culture systems, in silico approaches such as AI-based models, and improved in vitro assays—are gaining significant attention. This review systematically compares conventional 2D models, animal models, organ-on-a-chip systems, and computational models, highlighting their advantages, limitations, and predictive reliability. By critically evaluating these methodologies, we propose future directions for refining hepatotoxicity assessment strategies, with an emphasis on enhancing translational relevance, reducing reliance on animal testing, and integrating AI-driven predictive models.