Chen Xi, Roberts Ruth, Tong Weida, Liu Zhichao
National Center for Toxicological Research, Food and Drug Administration, Jefferson, Arkansas 72079, USA.
ApconiX Ltd, Alderley Edge SK10 4TG, UK.
Toxicol Sci. 2022 Mar 28;186(2):242-259. doi: 10.1093/toxsci/kfab157.
Animal studies are a critical component in biomedical research, pharmaceutical product development, and regulatory submissions. There is a worldwide effort in toxicology toward "reducing, refining, and replacing" animal use. Here, we proposed a deep generative adversarial network (GAN)-based framework capable of deriving new animal results from existing animal studies without additional experiments. To prove the concept, we employed this Tox-GAN framework to generate both gene activities and expression profiles for multiple doses and treatment durations in toxicogenomics (TGx). Using the pre-existing rat liver TGx data from the Open Toxicogenomics Project-Genomics-Assisted Toxicity Evaluation System (Open TG-GATES), we generated Tox-GAN transcriptomic profiles with high similarity (0.997 ± 0.002 in intensity and 0.740 ± 0.082 in fold change) to the corresponding real gene expression profiles. Consequently, Tox-GAN showed an outstanding performance in 2 critical TGx applications, gaining a molecular understanding of underlying toxicological mechanisms and gene expression-based biomarker development. For the former, over 87% agreement in Gene Ontology was found between Tox-GAN results and real gene expression data. For the latter, the concordance of biomarkers between real and generated data was high in both predictive performance and biomarker genes. We also demonstrated that the Tox-GAN models constructed with the Open TG-GATES data were capable of generating transcriptomic profiles reported in DrugMatrix. Finally, we demonstrated potential utility for Tox-GAN in aiding chemical-based read-across. To the best of our knowledge, the proposed Tox-GAN model is novel in its ability to generate in vivo transcriptomic profiles at different treatment conditions from chemical structures. Overall, Tox-GAN holds great promise for generating high-quality toxicogenomic profiles without animal experimentation.
动物研究是生物医学研究、药品开发和监管申报中的关键组成部分。毒理学领域正在全球范围内努力实现“减少、优化和替代”动物使用。在此，我们提出了一种基于深度生成对抗网络（GAN）的框架，该框架能够从现有的动物研究中得出新的动物实验结果，而无需进行额外实验。为了验证这一概念，我们使用这个Tox-GAN框架在毒理基因组学（TGx）中生成多剂量和不同处理持续时间下的基因活性和表达谱。利用来自开放毒理基因组学项目-基因组辅助毒性评估系统（Open TG-GATES）的已有大鼠肝脏TGx数据，我们生成了与相应真实基因表达谱具有高度相似性（强度方面为0.997±0.002，倍数变化方面为0.740±0.082）的Tox-GAN转录组谱。因此，Tox-GAN在两个关键的TGx应用中表现出色，即在深入了解潜在毒理机制和基于基因表达的生物标志物开发方面。对于前者，Tox-GAN结果与真实基因表达数据在基因本体论方面的一致性超过87%。对于后者，真实数据和生成数据之间的生物标志物在预测性能和生物标志物基因方面的一致性都很高。我们还证明，用Open TG-GATES数据构建的Tox-GAN模型能够生成DrugMatrix中报道的转录组谱。最后，我们展示了Tox-GAN在辅助基于化学物质的类推方面的潜在效用。据我们所知，所提出的Tox-GAN模型在从化学结构生成不同处理条件下的体内转录组谱的能力方面是新颖的。总体而言，Tox-GAN在不进行动物实验的情况下生成高质量毒理基因组谱方面具有很大潜力。
