A platform to predict side-effect targets for drugs

描述（由申请人提供）：生物活性小分子可以作用于多个靶标，这些脱靶活性是药物遭受的许多不良反应的基础。该建议的动机思想是，这些药物不良反应（ADR）的目标可以全面和系统地预测使用化学信息推理。我们开发的“相似性包围法”（SEA）基于其配体而不是其序列同一性，结构相似性，通路作用或功能对靶标进行分类，并预测否则无法获得且通常令人惊讶的关联。 在SBIR的第一阶段， 我们利用配体相似性构建了一个全局靶图谱，利用它来预测脱靶。 这张地图提出了几种药物的作用靶点机制，重新定位的候选者-第一阶段的目标-和预测的ADR协会。我们在此重点关注的正是后一个目标。广泛的初步结果，包括与制药公司的合作和概念验证联邦合作，支持利用这个平台预测不利脱靶的科学和财政实用主义。在这个项目的第二阶段，我们 开发直接的药物-靶点-ADR图谱，并开发新技术，使该方法更加稳健。具体目标是：1.创建完整的药物-靶点-ADR图，并演示概念验证。我们建议创建一个直接的药物靶点ADR地图。这将在所有获批药物和研究药物、所有可获得的靶点以及可能与靶点相关的所有不良反应中全面进行。我们预计，这些方法和该图谱最重要的商业用途将是优先考虑ADR靶点，以测试临床和临床前候选分子。为了证明对这些分子的概念，我们将测试研究药物调节预测ADR靶点的能力。 2.用新的方法和新的配体生理学数据库改进SEA。为了使配体-靶标-ADR关联图更加稳健，我们将改进SEA基础的方法和数据库。(a)我们将开发分子物理性质的描述符和过滤器，而不是单独使用配体拓扑结构。(b)我们将聚集目标配体，而不是假设它们总是形成一个单一的，有凝聚力的集合。(c)我们将把配体亲和力加权SEA和测试所产生的预测。(d)最后，我们将从预测的靶点特征中得出药物-ADR相关性，而不是仅仅依赖于单个靶点预测。 大量的初步结果支持我们的平台预测靶向药物不良事件的承诺。这些是临床试验中药物失败的最常见原因之一，因此对这种方法产生了极大的兴趣。这里提出的研究有可能大大提高该方法的广度和可靠性，并相应地提高其商业应用。

DESCRIPTION (provided by applicant): Bioactive small molecules can act on multiple targets, and these off-target activities underlie many of the adverse reactions from which drugs suffer. The motivating idea of this proposal is that these Adverse Drug Reaction (ADR) targets may be predicted comprehensively and systematically using chemoinformatic inference. The "Similarity Ensemble Approach" (SEA), developed by us, classifies targets based on their ligands rather than their sequence identity, structural similarity, pathway role or function, and predicts associations that are otherwise inaccessible and often surprising. In the first phase of this SBIR we constructed a global target map using ligand similarity, exploiting this to predict off-targets. This map suggested mechanism of action targets for several drugs, candidates for repositioning-both aims in the first phase-and predicted ADR associations. It is this latter goal that we focus on here. Extensive preliminary results, including a collaboration with pharma and a proof-of-concept federal collaboration, support the scientific and financial pragmatism of exploiting this platform for predicting adverse off-targets. In the second phase of this project we develop a direct drug-target-ADR map and develop new techniques to make the method more robust. The specific aims are: 1. To create a full drug-target-ADR map, and demonstrate proof-of-concept. We propose to create a direct drug-target-ADR map. This will be done comprehensively across all approved and investigational drugs, all accessible targets, and all adverse reactions for which targets may be associated. We anticipate that the most important commercial use of these methods and this map will be to prioritize ADR targets to test against for molecules that are clinical and preclinical candidates. To show proof of concept against such molecules, we will test investigational drugs for their ability to modulate ADR targets predicted for them. 2. To improve SEA with new methods and new ligand-physiology databases. To make the ligand- target-ADR association map more robust, we will improve the methods and databases underlying SEA. (a) We will develop descriptors of and filters for physical properties of molecules, rather than using ligand topology alone. (b) We will cluster target ligands, rather than assuming they always form a single, cohesive set. (c) We will incorporate ligand affinity weighting into SEA and test the resulting predictions. (d) Finally, we will derive drug-ADR associations from predicted target profiles, rather than relying on individual target predictions alone. Substantial preliminary results support the promise of our platform for predicting target-based drug adverse events. These are among the most common reasons for drug failures in clinical trials, and there has thus been great interest in this method. The studies proposed here have the potential to greatly improve the breadth and reliability of the method and, correspondingly, its commercial application.