Chandra Mansi, Li Ting, Tong Weida
National Center for Toxicological Research, Food and Drug Administration, Jefferson, AR 72079, United States.
University of Arkansas at Little Rock and University of Arkansas for Medical Sciences Joint Bioinformatics Program, Little Rock, AR 72204, United States.
Toxicol Sci. 2025 Oct 1;207(2):361-371. doi: 10.1093/toxsci/kfaf100.
In vitro to in vivo extrapolation (IVIVE) of toxicogenomics (TGx) data is essential for enhancing mechanism-based toxicity evaluations and minimizing animal use. However, translating in vitro findings to in vivo responses remains challenging. Generative adversarial networks (GANs) show potential in synthesizing gene expression data but often miss subtle, toxicologically relevant signals. We developed AIVIVE (artificial intelligence-aided IVIVE), a novel framework integrating GANs with local optimizers guided by biologically relevant gene modules to improve prediction accuracy. AIVIVE was trained using rat liver in vitro and in vivo transcriptomic data from the Open TG-GATEs (Toxicogenomics Project-Genomics-Assisted Toxicity Evaluation System) database. AIVIVE was evaluated using cosine similarity, root mean squared error (RMSE), and mean absolute percentage error (MAPE), demonstrating synthetic profiles comparable to real biological replicates. Notably, the model showed high overlap with differentially expressed genes, including Cytochrome P450 enzymes, which are often underrepresented in vitro. AIVIVE recapitulated in vivo CYP expression patterns, overcoming in vitro limitations. Further analysis revealed that AIVIVE captured liver-related pathways like bile secretion, steroid hormone biosynthesis, hepatitis C, and chemical carcinogenesis. It also captured gene expression changes linked to liver-specific adverse outcome pathways, such as Cyp2e1 upregulation in non-alcoholic fatty liver disease. Additionally, AIVIVE slightly outperformed real data in necrosis classification tasks, suggesting its potential for advancing toxicology predictions. These findings support AIVIVE as a tool for generating biologically relevant, in vivo-like profiles from in vitro data to enhance risk assessment, drug safety, and the 3Rs (reduce, replace, refine) principle.
毒理基因组学（TGx）数据的体外到体内外推（IVIVE）对于加强基于机制的毒性评估和减少动物使用至关重要。然而，将体外研究结果转化为体内反应仍然具有挑战性。生成对抗网络（GAN）在合成基因表达数据方面显示出潜力，但往往会遗漏微妙的、与毒理学相关的信号。我们开发了AIVIVE（人工智能辅助IVIVE），这是一个将GAN与由生物学相关基因模块引导的局部优化器相结合的新框架，以提高预测准确性。AIVIVE使用来自开放TG-GATEs（毒理基因组学项目-基因组学辅助毒性评估系统）数据库的大鼠肝脏体外和体内转录组数据进行训练。使用余弦相似度、均方根误差（RMSE）和平均绝对百分比误差（MAPE）对AIVIVE进行评估，结果表明合成谱与真实生物学重复相当。值得注意的是，该模型与差异表达基因有高度重叠，包括细胞色素P450酶，这些酶在体外通常代表性不足。AIVIVE概括了体内CYP表达模式，克服了体外局限性。进一步分析表明，AIVIVE捕捉到了与肝脏相关的途径，如胆汁分泌、类固醇激素生物合成、丙型肝炎和化学致癌作用。它还捕捉到了与肝脏特异性不良结局途径相关的基因表达变化，如非酒精性脂肪性肝病中Cyp2e1的上调。此外，AIVIVE在坏死分类任务中略优于真实数据，表明其在推进毒理学预测方面的潜力。这些发现支持AIVIVE作为一种工具，可从体外数据生成生物学相关的、类似体内的谱，以加强风险评估、药物安全性和3R（减少、替代、优化）原则。
