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:
- 金额:$ 18.6万
- 依托单位:
- 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:
- 关键词:AdjuvantAffectAnimalsAntibioticsAstronomyBacteriaBiological ModelsCandidate Disease GeneCell physiologyClinicalCollectionCombined Modality TherapyComputer ModelsDataDrug AntagonismDrug CombinationsDrug InteractionsDrug SynergismDrug TargetingGene Expression ProfileGenesGeneticGenomicsHair CellsHearingHomologous GeneHumanImageIndividualInfectionInjuryLabyrinthLarvaLibrariesLifeMachine LearningMalignant NeoplasmsMammalsMarketingMeasuresMedicineMethodsModelingMolecularMonitorMusOrganOrganismPatientsPharmaceutical PreparationsPhenotypeRegimenSamplingStainsSurveysSystemTestingToxic effectTrainingTranslatingTranslationsZebrafishantagonistantimicrobial drugclinical developmentcomputerized toolscosthuman modelin silicoin vivoin vivo Modelknock-downlateral lineloss of functionmodel buildingnovelnovel therapeuticsotoprotectantototoxicitypre-clinicalpre-clinical assessmentpredictive modelingrational designresponseside effectsynergismtooltranscriptometranscriptome 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.
摘要
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:
- 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
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Shuyi Ma其他文献
Shuyi Ma的其他文献
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{{ truncateString('Shuyi Ma', 18)}}的其他基金
Network Dissection of Host-Pathogen Interactions in Mycobacterium tuberculosis Infection
结核分枝杆菌感染中宿主-病原体相互作用的网络剖析
- 批准号:
10294557 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
Network Dissection of Host-Pathogen Interactions in Mycobacterium tuberculosis Infection
结核分枝杆菌感染中宿主-病原体相互作用的网络剖析
- 批准号:
10458724 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
Network Dissection of Host-Pathogen Interactions in Mycobacterium tuberculosis Infection
结核分枝杆菌感染中宿主-病原体相互作用的网络剖析
- 批准号:
10672236 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
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:
10322356 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
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:
9880239 - 财政年份:2021
- 资助金额:
$ 18.6万 - 项目类别:
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