A platform to predict side-effect targets for drugs

预测药物副作用目标的平台

• 批准号: 8738680
• 负责人: Carl Nicholas Hodge
• 金额: $ 45.35万
• 依托单位: SEACHANGE PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8738680
• 关键词: Adverse effects; Adverse event; Adverse reactions; Affinity; Binding; Clinical; Clinical Trials; Collaborations; Computing Methodologies; Databases; Descriptor; Development; Drug Targeting; Failure; Fingerprint; Funding; Goals; Individual; Investigational Drugs; Letters; Ligands; Manuscripts; Maps; Measures; Methods; Molecular; Pathway interactions; Pharmaceutical Preparations; Phase; Physiological; Physiology; Reaction; Relative (related person); Role; Safety; Site; Small Business Innovation Research Grant; Techniques; Test Result; Testing; Time; Weight; base; commercial application; drug candidate; drug discovery; improved; interest; physical property; pre-clinical; public health relevance; small molecule

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.

描述(由申请人提供)：生物活性小分子可以作用于多个靶点，这些非靶点活动是药物遭受的许多不良反应的基础。这一建议的动机是可以使用化学信息学推理来全面和系统地预测这些药物不良反应(ADR)靶点。由我们开发的“相似性集合方法”(SEA)根据它们的配体而不是它们的序列同一性、结构相似性、途径作用或功能来对目标进行分类，并预测否则无法获得且经常令人惊讶的关联。在此SBIR的第一阶段 我们利用配体的相似性构建了一个全局目标图，并利用这一点来预测非目标。 这张图建议了几种药物的作用靶点机制，重新定位的候选药物-这两个目标都在第一阶段-并预测了ADR相关性。我们在这里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的联邦合作，支持利用这一平台预测不利的偏离目标的科学和财务实用主义。在这个项目的第二阶段，我们 开发直接的药物靶标ADR图，并开发新技术以使该方法更加稳健。具体目标是：1.创建完整的药物-靶点-ADR图，并进行概念验证。我们建议创建一个直接的药物-靶点-ADR图。这将在所有批准和研究的药物、所有可接触的靶标和可能与靶标相关的所有不良反应中全面完成。我们预计，这些方法和这张图最重要的商业用途将是优先考虑ADR靶标，以测试临床和临床前候选分子。为了证明针对这些分子的概念，我们将测试研究药物调节为它们预测的ADR靶点的能力。2.用新的方法和新的配基-生理学数据库改进SEA。为了使配体-靶-ADR关联图更加稳健，我们将改进SEA下的方法和数据库。(A)我们将为分子的物理性质开发描述符和过滤器，而不仅仅是使用配体拓扑。(B)我们将目标配体聚集在一起，而不是假设它们总是形成一个单一的、有凝聚力的集合。(C)我们将把配基亲和力加权纳入SEA，并检验由此产生的预测。(D)最后，我们将从预测的靶标情况中得出药物与ADR的关联，而不是仅依赖单个靶标的预测。大量的初步结果支持了我们的平台在预测靶向药物不良事件方面的前景。这些都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。本文提出的研究有可能极大地提高该方法的广度和可靠性，并相应地提高其商业应用。