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: 10674484
• 负责人: GROVER P MILLER
• 金额: $ 36.94万
• 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/10674484
• 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; Marketing; Mediating; Medicare; Medicine; Metabolic; Metabolic Activation; Metabolic Biotransformation; Metabolism; Mining; Modeling; Molecular; Patients; Pattern; Pharmaceutical Preparations; Physiological; Process; Proteins; Reaction; Regimen; Risk; Role; Scientist; Structure; Syndrome; 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.

修改后的项目摘要/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)引起的不良反应是最难以预测的，有时甚至是严重的，也可能是致命的。超敏反应引起的不良反应是导致停药和终止临床开发的主要原因。然而，很大一部分药物与超敏反应驱动的不良反应无关，这为如果存在可靠的生物激活模式，新药可以完全避免这些不良反应带来了希望。对易发生ADR的分子的准确预测和识别将通过在与患者接触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。小分子本身并不具有免疫原性，而是将生物激活作用转化为反应性代谢产物，然后以共价方式修饰蛋白质以产生免疫原性抗原。“结构警报”是容易被生物激活的分子子结构，它们经常被用来识别容易被生物激活的小分子，并面临生物激活介导的不良反应的风险。目前，与生物激活相关的警报是由专家定义的，它们具有重要的限制，本研究克服了这些限制。现在，使用机器学习方法来预测新陈代谢、反应性和毒性是可能的。在此基础上，该建议通过明确建模新陈代谢对反应性的影响，从而有可能形成ADR相关加合物，系统地发现新的结构警报。我们假设(1)已知的生物激活反应，(2)分子引用数据，(3)新的子结构挖掘算法可以用于识别新出现的结构警报。目的1.我们将使用一种计算方法来系统地从已知新陈代谢和反应反应的数据库中挖掘结构性警报，以检验这一假设。目标2.我们将通过计算和实验验证结构性警报，并评估其结构性意外情况。结构警报只能有条件地被生物激活，这取决于它们出现的确切分子。此外，当有实验证据表明结构性警报实际上可以被生物激活时，新提出的结构性警报最有用。 PubHlthRel：这项研究中发现的结构性警报将帮助科学家在药物开发中避免有毒分子，并更好地了解市场上的药物为什么会变得有毒。克服了结构警报的根本限制，生物激活的机器学习模型将阐明哪些分子警报是生物激活的，哪些分子不是生物激活的。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。

项目成果

Discovery of Novel Reductive Elimination Pathway for 10-Hydroxywarfarin.

• DOI: 10.3389/fphar.2021.805133
• 发表时间: 2021
• 期刊: Frontiers in pharmacology
• 影响因子: 5.6
• 作者: Pouncey DL;Barnette DA;Sinnott RW;Phillips SJ;Flynn NR;Hendrickson HP;Swamidass SJ;Miller GP
• 通讯作者: Miller GP