Table_4_Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico.xlsx

Toxicogenomics (TGx) is a powerful method to evaluate toxicity and is widely used in both in vivo and in vitro assays. For in vivo TGx, reduction, refinement, and replacement represent the unremitting pursuit of live-animal tests, but in vitro assays, as alternatives, usually demonstrate poor correlation with real in vivo assays. In living subjects, in addition to drug effects, inner-environmental reactions also affect genetic variation, and these two factors are further jointly reflected in gene abundance. Thus, finding a strategy to factorize inner-environmental factor from in vivo assays based on gene expression levels and to further utilize in vitro data to better simulate in vivo data is needed. We proposed a strategy based on post‐modified non‐negative matrix factorization, which can estimate the gene expression profiles and contents of major factors in samples. The applicability of the strategy was first verified, and the strategy was then utilized to simulate in vivo data by correcting in vitro data. The similarities between real in vivo data and simulated data (single-dose 0.72, repeat-doses 0.75) were higher than those observed when directly comparing real in vivo data with in vitro data (single-dose 0.56, repeat-doses 0.70). Moreover, by keeping environment-related factor, a simulation can always be generated by using in vitro data to provide potential substitutions for in vivo TGx and to reduce the launch of live-animal tests.

毒理基因组学（Toxicogenomics, TGx）是评估毒性的高效手段，广泛应用于体内（in vivo）与体外（in vitro）实验体系。针对体内毒理基因组学研究，减少、优化与替代是活体动物实验的不懈追求，但作为替代方案的体外实验通常与真实体内实验的相关性较差。在活体生物中，除药物作用外，内环境反应同样会影响遗传变异，这两类因素最终共同体现在基因丰度的变化上。因此，亟需开发一种策略，能够基于基因表达水平从体内实验中分离内环境因素，并进一步利用体外数据更精准地模拟体内数据。本研究提出了一种基于后修正非负矩阵分解（post-modified non-negative matrix factorization）的策略，可估算样本中的基因表达谱及主要因素的含量。首先验证了该策略的适用性，随后通过校正体外数据，利用该策略模拟体内数据。真实体内数据与模拟数据之间的相似度（单次给药组为0.72、多次给药组为0.75），高于直接对比真实体内数据与体外数据时的相似度（单次给药组为0.56、多次给药组为0.70）。此外，通过保留环境相关因素，始终可利用体外数据生成模拟结果，为体内毒理基因组学研究提供潜在替代方案，从而减少活体动物实验的开展。