Predicting DILI liability by transcription factor profiling

项目总结 药物性肝损伤(DILI)是药物在发育过程中磨损的主要原因，也是导致药物磨损的主要原因 上市后停药的原因。在这里，我们提出了一种检测候选药物的系统生物学方法。 帝力债务处于发展的早期阶段。这一方法是基于对药物诱导的 肝细胞中多条信号转导通路的扰动。为此，我们使用Attagene多路传输 Report技术，即阶乘™，能够定量评估多个 转录因子(TF)，调节基因转录的蛋白质。阶乘已被广泛应用于 通过为美国环保局ToxCast项目筛选数千种环境毒物而得到验证。通过这件事 经过努力，我们发现了许多类别生物活动的特定“TF签名”。 在初步研究中，我们评估了一小部分有DILI责任的药物的TF签名，并发现 一种常见的模式。在一定的浓度范围内，药物的Tf信号反映了药物的主要活性。 然而，在某些拐点(COFF)，这些签名转变为不同的、脱离目标的签名。我们 发现不同类别的帝力药物共有的共同的非靶标Tf特征，并确定了潜在的 一些常见的转铁蛋白信号的机制，包括线粒体故障，DNA损伤，以及 脂质过氧化。基于这些发现，我们开发了一个简单的模型，其中推断了DILI机制 而DILI概率由CMAX/COFF比率定义，其中CMAX是 最大治疗药物浓度。最值得注意的是，我们的数据表明，使用该模型 预测特殊的帝力，这是现有技术无法完成的任务。 这项提议的首要目标是建立专题工作组概况，作为帝力指数预测的工具。去做 我们将获得FDA归类为DILI和非DILI的396种药物的TF签名 毒品。这些签名将用作训练集。我们将确定特定于帝力的共同脱离目标的集群 TF签名，并使用参考TF签名的Attagene DB注释潜在的生物学活性。 为了验证非靶标Tf签名作为潜在的生物活性标记，我们将通过以下方式将这些签名与数据进行比较 已知的DILI机制的功能分析。此外，我们还将确定 用于区分DILI和非DILI药物的CMAX/COFF参数。获得的DILI特异性转铁蛋白的预测价值 签名和CMAX/COFF参数将使用一组在 功能分析候选药物，由制药行业和帝力-西姆财团提供。

PROJECT SUMMARY Drug-induced liver injury (DILI) is the main reason for drug attrition during development and a leading cause of post-market drug withdrawal. Here, we propose a systems biology approach to detect drug candidates with DILI liabilities at early stages of development. This approach is based on the assessment of drug-induced perturbations of multiple signal transduction pathways in hepatocytic cells. For that, we use Attagene multiplexed reporter technology, the FACTORIAL™,that enables quantitative assessment of the activity of multiple transcription factors (TFs), proteins that regulate gene transcription. The FACTORIAL has been extensively validated by screening thousands of environmental toxicants for the U.S. EPA ToxCast project. Through this effort, we discovered specific “TF signatures” for many classes of biological activities. In preliminary studies, we evaluated TF signatures for a small panel of drugs with DILI liabilities and found a common pattern. Within certain concentration range, drugs' TF signatures reflected their primary activities. However, at some inflection points (COFF), these signatures transformed into distinct, off-target, signatures. We found common off-target TF signatures shared by different classes of DILI drugs and identified underlying mechanisms for some of those common TF signatures, including mitochondrial malfunction, DNA damage, and lipid peroxidation. Based on these findings, we developed a simple model wherein DILI mechanism is inferred from the off-target TF signature, whilst DILI probability is defined by the CMAX/COFF ratio, where CMAX is the maximal therapeutic drug concentration. Most remarkably, our data suggest the feasibility of using this model to predict idiosyncratic DILI, the task unattainable with existing technologies. The overarching objective of this proposal is to establish TF profiling as a tool for DILI prediction. To do that, we will obtain TF signatures of a collection of 396 drugs classified by the FDA as DILI and no-DILI concern drugs. These signatures will be used as a training set. We will identify clusters of common DILI-specific off-target TF signatures and annotate the underlying biological activities, using ATTAGENE DB of reference TF signatures. To validate the off-target TF signatures as potential bioactivity markers, we will compare these with data by functional assays for known DILI mechanisms. Furthermore, we will determine the predictive value for the CMAX/COFF parameter for stratifying DILI from non-DILI drugs. The predictive values of obtained DILI-specific TF signatures and the CMAX/COFF parameter will be optimized using a validation set of exhaustively characterized in functional assays drug candidates, provided by pharmaceutical industry and DILI-sim consortia.