Systematic Discovery of Bioactivation-Associated Structural Alerts

生物活化相关结构警报的系统发现

• 批准号: 10491726
• 负责人: GROVER P MILLER
• 金额: $ 37.48万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491726
• 关键词: Acute Liver Failure; Algorithm Design; Algorithms; Antigens; Binding Proteins; Cell Death; Cessation of life; Chemical Models; Chemicals; Dangerousness; Data; Databases; Dose; Exanthema; Exposure to; Fostering; Foundations; Future; Health; Hepatotoxicity; Hospitalization; Hypersensitivity; Immune; Immune response; Knowledge; Length of Stay; Machine Learning; Manuals; Mediating; Medicare; Medicine; Metabolic; Metabolic Activation; Metabolism; Mining; Modeling; Molecular; Patients; Pattern; Pharmaceutical Preparations; Physiological; Process; Proteins; Reaction; Regimen; Risk; Role; Scientist; Stevens-Johnson Syndrome; Structure; Techniques; Testing; Toxic effect; United States; adduct; adverse drug reaction; clinical development; clinically relevant; data mining; deep learning model; design; drug development; drug induced liver injury; drug market; drug modification; drug withdrawal; experimental study; gene function; immunogenic; immunogenicity; improved; in vitro Assay; interdisciplinary approach; machine learning model; mathematical model; protein function; public health relevance; screening; small molecule

Modified Project Summary/Abstract Section Adverse drug reactions (ADRs) are dangerous and expensive. ADRS driven by immune-mediated hypersensitivity (including rashes, hepatotoxicity, and Steven-Johnson syndrome) are the most difficult to predict and occasionally can be severe as well as fatal. Hypersensitivity-driven ADRs are the leading cause of drug withdrawal and termination of clinical development. Yet a large proportion of drugs are not associated with hypersensitivity-driven ADRs, offering hope that new medicines could avoid these ADRs entirely if reliable models of bioactivation existed. Accurate prediction and identification of molecules prone to ADRs would revolutionize drug development by screening out ADR-prone candidates early, before exposure to patients, and guiding drug modifications to reduce ADR risk. Small molecules are not intrinsically immunogenic and instead, involve bioactivation into reactive metabolite is that then covalently modify proteins to create immunogenic antigens. “Structural alerts” are molecular substructures prone to bioactivation, and they are often used to identify small molecules prone to bioactivation, and at risk of bioactivation-mediated ADRs. Currently, bioactivation relevant alerts are defined by experts, and they have important limitations that this study overcomes. It is now possible to predict metabolism and reactivity and toxicity using machine learning approaches. Building on this foundation, this proposal systematically discovers new structural alerts by explicitly modeling the impact of metabolism on reactivity and hence the potential to form ADR-relevant adducts. We hypothesize that (1) known bioactivation reactions, (2) molecule citation data, and (3) new substructure mining algorithms can be used to identify emerging structural alerts. Aim 1. We will test this hypothesis by using a computational approach to systematically mine structural alerts from databases of known metabolism and reactivity reactions. Aim 2. We will computationally and experimentally validate structural alerts and assess their structural contingencies. Structural alerts are only conditionally bioactivated, depending on the precise molecule they appear. Newly proposed structural alerts, moreover, are most useful when there is experimental evidence that they in fact can be bioactivated. PubHlthRel: Structural alerts discovered in this study will help scientists avoid toxic molecules in drug development, and better understand why medicines on the market become toxic. Overcoming a fundamental limitation with structural alerts, machine learning models of bioactivation will clarify in which molecules alerts are and are not bioactivated. This knowledge will help scientists make safer medicines in the future, modify existing medicines to make them safer, and reduce ADRs by using existing medicines more safely.

修改项目摘要/摘要部分 药物不良反应（ADR）是危险和昂贵的。由免疫介导的超敏反应（包括皮疹、肝毒性和Steven-Johnson综合征）驱动的ADRS是最难预测的，偶尔可能是重度和致死性的。超敏反应导致的ADR是导致停药和临床开发终止的主要原因。然而，很大一部分药物与超敏反应驱动的ADR无关，如果存在可靠的生物活化模型，新药可以完全避免这些ADR。准确预测和鉴定易发生ADR的分子将通过在暴露于患者之前及早筛选出易发生ADR的候选药物，并指导药物修改以降低ADR风险，从而彻底改变药物开发。小分子本身不是免疫原性的，而是涉及生物活化成反应性代谢物，然后共价修饰蛋白质以产生免疫原性抗原。“结构警报”是易于生物活化的分子亚结构，并且它们通常用于鉴定易于生物活化并且处于生物活化介导的ADR的风险中的小分子。目前，生物活化相关警报由专家定义，它们具有本研究克服的重要局限性。现在可以使用机器学习方法来预测代谢、反应性和毒性。在此基础上，该提案通过明确建模代谢对反应性的影响以及形成ADR相关加合物的可能性，系统地发现新的结构警报。我们假设（1）已知的生物活化反应，（2）分子引用数据，和（3）新的子结构挖掘算法可用于识别新出现的结构警报。目标1.我们将测试这一假设，通过使用计算方法，系统地挖掘已知的代谢和反应性反应的数据库中的结构警报。目标2.我们将通过计算和实验验证结构警报，并评估其结构应急。结构警报仅被条件性地生物激活，这取决于它们出现的精确分子。此外，新提出的结构警报在有实验证据表明它们实际上可以被生物激活时最有用。 PubHlthRel：这项研究中发现的结构警报将帮助科学家在药物开发中避免有毒分子，并更好地理解为什么市场上的药物会有毒。克服结构警报的基本限制，生物活化的机器学习模型将澄清哪些分子警报是生物活化的，哪些不是生物活化的。这些知识将帮助科学家在未来制造更安全的药物，修改现有药物使其更安全，并通过更安全地使用现有药物来减少ADR。