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.

修改的项目摘要/摘要部分 不良药物反应（ADR）是危险且昂贵的。由免疫介导的超敏反应（包括皮疹，肝毒性和史蒂芬·约翰逊综合症）驱动的ADR是最难预测的ADR，偶尔也可能是严重且致命的。超敏反应的ADR是药物戒断和终止临床发育的主要原因。然而，很大一部分药物与超敏反应的ADR无关，可以希望如果存在可靠的生物活化模型，则希望新药物可以完全避免使用这些ADR。易于使用ADR的分子的准确预测和鉴定将通过早日筛选出易ADR的候选者，暴露于患者之前以及指导药物修改以降低ADR风险，从而彻底改变药物的开发。小分子不是本质上的免疫原性，而是涉及生物活化到反应性代谢物中的是，然后共价修改蛋白质以产生免疫原性抗原。 “结构警报”是容易生物活化的分子亚结构，通常用于鉴定容易生物活化的小分子，并且有生物活化介导的ADR的风险。当前，生物活化相关的警报是由专家定义的，它们具有本研究的重要局限性。现在可以使用机器学习方法来预测代谢，反应性和毒性。该提案以这个基础为基础，系统地通过明确建模代谢对反应性的影响以及形成与ADR相关的加合物的潜力来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）分子引文数据和（3）新的子结构挖掘算法可用于识别新兴的结构警报。目标1。我们将通过使用计算方法来检验该假设，从而从已知的代谢和反应反应数据库中系统地挖掘结构警报。 AIM 2。我们将在计算和实验上验证结构警报并评估其结构意外情况。结构警报仅在有条件地生化，具体取决于它们出现的精确分子。此外，当有实验证据表明它们实际上可以被生物活化时，新提出的结构警报最有用。 PubHlthrel：本研究中发现的结构警报将有助于科学家避免在药物开发中的有毒分子，并更好地理解为什么市场上的药物会变得有毒。通过结构警报来克服基本限制，生物活化的机器学习模型将阐明分子警报在哪些情况下是并且未被生物激活。这些知识将在将来帮助科学家制造安全的药物，修改现有药物以使其安全，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