Mulliner Denis, Schmidt Friedemann, Stolte Manuela, Spirkl Hans-Peter, Czich Andreas, Amberg Alexander
R&D DSAR/Preclinical Safety FF, Sanofi-Aventis Deutschland GmbH , Industriepark Hoechst, Building H831, D-65926 Frankfurt am Main, Germany.
Chem Res Toxicol. 2016 May 16;29(5):757-67. doi: 10.1021/acs.chemrestox.5b00465. Epub 2016 Apr 6.
Hepatic toxicity is a key concern for novel pharmaceutical drugs since it is difficult to anticipate in preclinical models, and it can originate from pharmacologically unrelated drug effects, such as pathway interference, metabolism, and drug accumulation. Because liver toxicity still ranks among the top reasons for drug attrition, the reliable prediction of adverse hepatic effects is a substantial challenge in drug discovery and development. To this end, more effort needs to be focused on the development of improved predictive in-vitro and in-silico approaches. Current computational models often lack applicability to novel pharmaceutical candidates, typically due to insufficient coverage of the chemical space of interest, which is either imposed by size or diversity of the training data. Hence, there is an urgent need for better computational models to allow for the identification of safe drug candidates and to support experimental design. In this context, a large data set comprising 3712 compounds with liver related toxicity findings in humans and animals was collected from various sources. The complex pathology was clustered into 21 preclinical and human hepatotoxicity endpoints, which were organized into three levels of detail. Support vector machine models were trained for each endpoint, using optimized descriptor sets from chemometrics software. The optimized global human hepatotoxicity model has high sensitivity (68%) and excellent specificity (95%) in an internal validation set of 221 compounds. Models for preclinical endpoints performed similarly. To allow for reliable prediction of "truly external" novel compounds, all predictions are tagged with confidence parameters. These parameters are derived from a statistical analysis of the predictive probability densities. The whole approach was validated for an external validation set of 269 proprietary compounds. The models are fully integrated into our early safety in-silico workflow.
肝毒性是新型药物的一个关键问题，因为在临床前模型中很难预测，而且它可能源于与药理无关的药物效应，如途径干扰、代谢和药物蓄积。由于肝毒性仍然是药物淘汰的主要原因之一，因此在药物研发中可靠预测肝脏不良反应是一项重大挑战。为此，需要更多努力集中在改进的体外和计算机模拟预测方法的开发上。当前的计算模型往往不适用于新型药物候选物，这通常是由于感兴趣的化学空间覆盖不足，这是由训练数据的大小或多样性造成的。因此，迫切需要更好的计算模型来识别安全的药物候选物并支持实验设计。在此背景下，从各种来源收集了一个包含3712种在人和动物中具有肝脏相关毒性发现的化合物的大数据集。复杂的病理情况被聚类为21个临床前和人类肝毒性终点，并被组织成三个详细程度级别。使用化学计量学软件的优化描述符集为每个终点训练支持向量机模型。在一个由221种化合物组成的内部验证集中，优化后的全球人类肝毒性模型具有高灵敏度（68%）和出色的特异性（95%）。临床前终点的模型表现类似。为了可靠预测“真正外部的”新型化合物，所有预测都标记有置信参数。这些参数来自对预测概率密度的统计分析。整个方法在一个由269种专利化合物组成的外部验证集中得到了验证。这些模型已完全集成到我们早期的计算机模拟安全工作流程中。
