Predicting DILI liability by transcription factor profiling

项目摘要 药物性肝损伤（DILI）是药物开发过程中药物消耗的主要原因， 上市后药品撤回的原因。在这里，我们提出了一个系统生物学方法来检测候选药物 在发展的早期阶段有DILI负债。这种方法是基于对药物诱导的 肝细胞中多种信号转导途径的扰动。为此，我们使用Attagene复用 报告基因技术，FACTORIAL™，其能够定量评估多个基因的活性， 转录因子（TF），调节基因转录的蛋白质。该因素已被广泛 通过为美国EPA ToxCast项目筛选数千种环境毒物进行验证。通过这个 通过这些努力，我们发现了许多生物活动的特定“TF签名”。 在初步研究中，我们评估了一小组具有DILI倾向的药物的TF特征，发现 一个共同的模式。在一定浓度范围内，药物的TF特征反映了其主要活性。 然而，在某些拐点（COFF），这些特征转化为不同的脱靶特征。我们 发现了不同类别DILI药物共有的共同脱靶TF特征，并确定了潜在的 一些常见TF特征的机制，包括线粒体功能障碍，DNA损伤， 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DILI机制推断 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的可行性， 预测特异性DILI，这是现有技术无法实现的任务。 该提案的总体目标是建立TF分析作为DILI预测的工具。做 我们将获得FDA归类为DILI和非DILI关注的396种药物的TF签名 毒品这些特征将用作训练集。我们将识别常见的DILI特异性脱靶簇 TF签名并使用参考TF签名的ATTAGENE DB注释潜在的生物活性。 为了验证脱靶TF特征作为潜在的生物活性标记物，我们将通过以下方法将这些与数据进行比较： 已知DILI机制的功能测定。此外，我们还将确定 用于将DILI与非DILI药物分层的CMAX/COFF参数。获得的DILI特异性TF的预测值 特征和CMAX/COFF参数将使用一组详尽表征的验证集进行优化， 功能测定药物候选物，由制药工业和DILI-sim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.