A platform to predict side-effect targets for drugs

预测药物副作用目标的平台

• 批准号: 8455865
• 负责人: Carl Nicholas Hodge
• 金额: $ 45.62万
• 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 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8455865
• 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标，而这些脱离目标的活动是药物遭受的许多不良反应。该提议的激励观念是，可以使用化学信息推理对这些不良药物反应（ADR）靶标进行全面和系统地预测。我们开发的“相似性合奏方法”（SEA）基于其配体而不是其序列身份，结构相似性，途径角色或功能对靶标进行了分类，并预测了这些关联，这些关联是无法访问的，而且通常令人惊讶。 在此Sbir的第一阶段 我们使用配体相似性构建了一个全局目标图，并利用了这一点以预测脱靶。 该地图提出了几种药物的作用靶标的机理，即重新定位的候选者在第一个阶段和预测的ADR关联中。我们关注的是后一个目标。广泛的初步结果，包括与Pharma的合作和概念验证联邦合作，支持利用该平台来预测不良目标的科学和财务实用主义。在这个项目的第二阶段，我们 开发直接的药品目标-ADR图，并开发新技术以使该方法更强大。具体目的是：1。创建一个完整的药品目标-ADR图，并展示概念验证。我们建议创建直接的药品目标-ADR图。这将在所有批准的和研究的药物，所有可访问的目标以及可能相关的所有不良反应中进行全面进行。我们预计这些方法最重要的商业用途和该地图将是优先考虑ADR靶标，以测试针对临床和临床前候选者的分子。为了显示针对此类分子的概念证明，我们将测试研究药物的调节ADR靶标的能力。 2。通过新方法和新的配体生理数据库改善海洋。为了使配体目标-ADR协会映射更强大，我们将改善海上的方法和数据库。 （a）我们将开发分子物理特性的描述符和过滤器，而不是单独使用配体拓扑。 （b）我们将聚集目标配体，而不是假设它们始终形成一个单一的凝聚力集。 （c）我们将将配体亲和力加权纳入海洋并测试结果预测。 （d）最后，我们将从预测的目标谱中得出药物-ADR关联，而不是仅依靠单独的目标预测。 实质性初步结果支持我们平台预测基于目标药物不良事件的希望。这些是临床试验中药物失败的最常见原因之一，因此对这种方法引起了极大的兴趣。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。