Transcriptome-driven inference of adverse drug interactions

转录组驱动的药物不良相互作用的推断

基本信息

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
项目类别：
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

项目摘要

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.

抽象的耳毒性是超过 150 种药物的一种使人衰弱的副作用，其中许多药物是作为多种药物的一部分开出的。治疗多种疾病的药物方案，包括癌症和顽固性感染。增强耳毒性的药物相互作用（DDI）使多药治疗方案的实施变得复杂，特别是在治疗多种并发疾病时，目前 DDI 只能在以下情况后才能检测到。药物已上市，因此识别潜在不良相互作用的有效临床前方法将促进更安全的联合处方数量庞大的组合使得可以测量所有可能的药物。相互作用是不可行的，因此根据单个化合物的数据预测耳毒性药物如何相互作用是不可行的目前对特定药物类别的耳毒性机制的了解是必要的。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耳保护佐剂，该策略不能预测不良耳毒性 DDI 或发展用于其他研究较少的药物类别和临床开发中的一流药物的耳保护剂。为了更广泛地调查潜在的耳毒性 DDI，我们将采用 INDIGO（使用推断药物相互作用） chemo-Genomics and Orthology），一种机器学习工具，目前可以预测协同/拮抗作用多种细菌物种的抗菌药物活性，无需特定的药物靶点信息。我们追求的是，我们可以利用基本方法来预测潜在的不利（协同）或通过基于数据构建“INDIGO-Tox”模型，对人类产生保护性（拮抗性）耳毒性 DDI 我们将测量 15 种药物引发的转录谱。已知具有耳毒性或耳保护作用，以及相应的成对耳毒性 DDI 表型斑马鱼，一个完善的用于研究耳毒性的体内模型系统，我们将使用这些数据来训练。然后，我们将使用 INDIGO-Tox 来预测 10 种其他药物之间的 DDI。我们将验证斑马鱼转录组响应曲线作为输入数据。我们还将在完善的小鼠离体耳毒性模型中翻译最有效的预测。使用该模型生成影响耳毒性的新基因的预测，然后我们将对其进行测试斑马鱼的成功完成将产生转化为人类的假设，促进模型的发展。扩大到评估更广泛的药物库中可能的耳毒性相互作用，并将建立一条途径预测耳毒性和其他器官毒性之间的相互作用。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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