Transcriptome-driven inference of adverse drug interactions

转录组驱动的药物不良相互作用的推断

• 批准号: 10541237
• 负责人: Shuyi Ma
• 金额: $ 18.6万
• 依托单位: SEATTLE CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-02 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10541237
• 关键词: Adjuvant; Affect; Animals; Antibiotics; Astronomy; Bacteria; Biological Models; Candidate Disease Gene; Cell physiology; Clinical; Collection; Combined Modality Therapy; Computer Models; Data; Drug Antagonism; Drug Combinations; Drug Interactions; Drug Synergism; Drug Targeting; Gene Expression Profile; Genes; Genetic; Genomics; Hair Cells; Hearing; Homologous Gene; Human; Image; Individual; Infection; Injury; Labyrinth; Larva; Libraries; Life; Machine Learning; Malignant Neoplasms; Mammals; Marketing; Measures; Medicine; Methods; Modeling; Molecular; Monitor; Mus; Organ; Organism; Patients; Pharmaceutical Preparations; Phenotype; Regimen; Sampling; Stains; Surveys; System; Testing; Toxic effect; Training; Translating; Translations; Zebrafish; antagonist; antimicrobial drug; clinical development; computerized tools; cost; human model; in silico; in vivo; in vivo Model; knock-down; lateral line; loss of function; model building; novel; novel therapeutics; otoprotectant; ototoxicity; pre-clinical; pre-clinical assessment; predictive modeling; rational design; response; side effect; synergism; tool; transcriptome; transcriptome sequencing

ABSTRACT Ototoxicity is a debilitating side effect of over 150 medications, many of which are prescribed as part of multi- drug regimens to treat a broad range of conditions including cancer and recalcitrant infections. Adverse drug- drug interactions (DDIs) that potentiate ototoxicity complicate the implementation of multi-drug regimens, particularly to treat multiple concurrent conditions. In most cases, DDIs are currently detected only after the drugs are on the market, so effective preclinical methods to identify potential adverse interactions would facilitate safer co-prescriptions. The astronomical number of combinations renders measuring all possible drug interactions infeasible, so predicting how ototoxic drugs interact from data of individual compounds is necessary. While the current understanding of mechanisms underlying ototoxicity of specific drug classes has helped to explain clinical observations of specific adverse ototoxicity DDIs, and aided rational design of candidate otoprotective adjuvants, this strategy cannot anticipate adverse ototoxicity DDIs or develop otoprotectants for other lesser studied drug classes and first-in-class drugs under clinical development. To survey more broadly for potential ototoxicity DDIs, we will adapt INDIGO (Inferring Drug Interactions using chemo-Genomics and Orthology), a machine learning tool that currently can predict synergy/antagonism of antimicrobial drug activity in multiple bacterial species without requiring specific drug target information. We hypothesize that we can harness the underlying approach to predict potentially adverse (synergistic) or protective (antagonistic) ototoxic DDIs in humans, by building an “INDIGO-Tox” model based on data generated from an appropriate animal system. We will measure transcriptional profiles elicited by 15 drugs known to convey ototoxicity or otoprotection, as well as corresponding pairwise ototoxicity DDI phenotypes in zebrafish, a well-established in vivo model system for studying ototoxicity. We will use these data to train INDIGO-Tox model. We will then use INDIGO-Tox to predict DDIs between 10 additional drugs, using their zebrafish transcriptome response profiles as input data. We will validate predictions in zebrafish, and will test translation of top validated predictions in a well-established mouse ex vivo model of ototoxicity. We will also use the model to generate predictions for novel genes that influence ototoxicity, which we will then test in zebrafish. Successful completion will generate hypotheses for translation into humans, facilitate model expansion to assessing possible ototoxic interactions for a broader library of drugs, and will establish a path to predict interactions between ototoxicity and other organ toxicities.

抽象的 耳毒性是150多种药物的令人衰弱的副作用，其中许多药物是多数毒性的一部分 药物方案以治疗广泛的疾病，包括癌症和顽固感染。不良药物 - 潜在的耳毒性使多药方案的实施复杂化的药物相互作用（DDI）， 在大多数情况下，目前仅在 药物在市场上，因此可以识别潜在不良相互作用的有效临床前方法 促进更安全的共同说明。组合的天文数量渲染，测量所有可能的药物 相互作用是不可行的，因此预测耳毒性药物如何与各个化合物数据相互作用是 必要的。虽然当前对特定药物类耳毒性基础机制的理解已有 有助于解释特定不良耳毒性DDI的临床观察结果，并有助于合理设计 候选辅助佐剂，该策略无法预期不良耳毒性DDI或发展 在临床发育下，用于其他较小研究的药物类别和一流的药物的耳触状剂。到 对潜在的耳毒性DDI的调查更广泛地调查，我们将适应Indigo（使用 化学基因组学和矫形学），一种机器学习工具，目前可以预测协同/对抗 多种细菌物种中的抗菌药物活性，而无需特定的药物靶向信息。我们 假设我们可以利用潜在的不利（协同）或 人类中的保护性（拮抗）耳毒性DDI，通过基于数据构建“靛蓝-TOX”模型 由适当的动物系统产生。我们将测量15种药物引起的转录曲线 已知可以传达耳毒性或耳毒性，以及相应的成对耳毒性DDI表型 斑马鱼，一种用于研究耳毒性的体内模型系统。我们将使用这些数据训练 靛蓝模型。然后，我们将使用Indigo-Tox来预测10种其他药物之间的DDI 斑马鱼转录组响应概况作为输入数据。我们将在斑马鱼中验证预测，并将测试 在良好的小鼠外毒性模型中的最高验证预测的翻译。我们也会 使用该模型为影响耳毒性的新基因生成预测，然后我们将在该基因中进行测试 斑马鱼。成功完成将产生转化为人类的假设，促进模型 扩展以评估更广泛的药物库的可能的耳毒性相互作用，并将建立通往的途径 预测耳毒性与其他器官毒性之间的相互作用。

项目成果

In vivo screening for toxicity-modulating drug interactions identifies antagonism that protects against ototoxicity in zebrafish.

毒性调节药物相互作用的体内筛选确定了防止斑马鱼耳毒性的拮抗作用。

• DOI: 10.1101/2023.11.08.566159
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Bustad,Ethan;Mudrock,Emma;Nilles,ElizabethM;McQuate,Andrea;Bergado,Monica;Gu,Alden;Galitan,Louie;Gleason,Natalie;Ou,HenryC;Raible,DavidW;Hernandez,RafaelE;Ma,Shuyi
• 通讯作者: Ma,Shuyi