Fielden Mark R, Eynon Barrett P, Natsoulis Georges, Jarnagin Kurt, Banas Deborah, Kolaja Kyle L
Iconix Pharmaceuticals, Inc., 325 East Middlefield Road, Mountain View, California 94043, USA.
Toxicol Pathol. 2005;33(6):675-83. doi: 10.1080/01926230500321213.
One application of genomics in drug safety assessment is the identification of biomarkers to predict compound toxicity before it is detected using traditional approaches, such as histopathology. However, many genomic approaches have failed to demonstrate superiority to traditional methods, have not been appropriately validated on external samples, or have been derived using small data sets, thus raising concerns of their general applicability. Using kidney gene expression profiles from male SD rats treated with 64 nephrotoxic or non-nephrotoxic compound treatments, a gene signature consisting of only 35 genes was derived to predict the future development of renal tubular degeneration weeks before it appears histologically following short-term test compound administration. By comparison, histopathology or clinical chemistry fails to predict the future development of tubular degeneration, thus demonstrating the enhanced sensitivity of gene expression relative to traditional approaches. In addition, the performance of the signature was validated on 21 independent compound treatments structurally distinct from the training set. The signature correctly predicted the ability of test compounds to induce tubular degeneration 76% of the time, far better than traditional approaches. This study demonstrates that genomic data can be more sensitive than traditional methods for the early prediction of compound-induced pathology in the kidney.
基因组学在药物安全性评估中的一个应用是识别生物标志物，以便在使用传统方法（如组织病理学）检测到化合物毒性之前预测其毒性。然而，许多基因组学方法未能证明其优于传统方法，未在外部样本上得到适当验证，或使用小数据集得出，因此引发了对其普遍适用性的担忧。利用接受64种肾毒性或非肾毒性化合物处理的雄性SD大鼠的肾脏基因表达谱，得出了一个仅由35个基因组成的基因特征，用于预测在短期给予受试化合物后，肾小管变性在组织学上出现前数周的未来发展情况。相比之下，组织病理学或临床化学无法预测肾小管变性的未来发展，从而证明了基因表达相对于传统方法具有更高的敏感性。此外，该特征的性能在21种与训练集结构不同的独立化合物处理上得到了验证。该特征正确预测受试化合物诱导肾小管变性能力的概率为76%，远优于传统方法。这项研究表明，对于早期预测肾脏中化合物诱导的病理情况，基因组数据可能比传统方法更敏感。
