Translational research in toxicology has significantly benefited from transcriptomic profiling, particularly in drug safety. However, its application has predominantly focused on limited organs, notably the liver, due to resource constraints. This paper presents TransTox, an innovative AI model using a generative adversarial network (GAN) method to facilitate the bidirectional translation of transcriptomic profiles between the liver and kidney under drug treatment. TransTox demonstrates robust performance, validated across independent datasets and laboratories. First, the concordance between real experimental data and synthetic data generated by TransTox was demonstrated in characterizing toxicity mechanisms compared to real experimental settings. Second, TransTox proved valuable in gene expression predictive models, where synthetic data could be used to develop gene expression predictive models or serve as “digital twins” for diagnostic applications. The TransTox approach holds the potential for multi-organ toxicity assessment with AI and advancing the field of precision toxicology.

毒理学的转化研究从转录组学分析中受益匪浅，尤其是在药物安全性方面。然而，由于资源限制，其应用主要集中在有限的器官上，尤其是肝脏。本文介绍了 TransTox，这是一种使用生成对抗网络 （GAN） 方法的创新 AI 模型，可促进药物治疗下肝脏和肾脏之间转录组图谱的双向翻译。TransTox 展示了强大的性能，并在独立数据集和实验室中进行了验证。首先，与真实实验环境相比，在表征毒性机制时证明了真实实验数据和 TransTox 生成的合成数据之间的一致性。其次，TransTox 在基因表达预测模型中被证明很有价值，其中合成数据可用于开发基因表达预测模型或用作诊断应用的“数字孪生”。TransTox 方法具有使用 AI 进行多器官毒性评估的潜力，并推动了精准毒理学领域的发展。