Developing a systems biology platform for predicting, preventing, and treating drug side effects

开发用于预测、预防和治疗药物副作用的系统生物学平台

基本信息

批准号：
9922312
负责人：
Aarash Bordbar
金额：
$ 75.45万
依托单位：
SINOPIA BIOSCIENCES, INC.
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-15 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9922312
关键词：
Adverse drug effect Adverse event Affect Algorithms Animal Model Antidepressive Agents Antineoplastic Agents Antipsychotic Agents Biochemical Bioinformatics Cell physiology Cessation of life Clinical Clinical Trials Complex Corpus striatum structure Coupled Data Data Set Databases Development Dose Dyskinetic syndrome Economic Burden Etiology Exposure to Expression Profiling Failure Functional disorder Funding Mechanisms Gene Expression Gene Expression Profile Generations Genes Gold Health Care Costs Healthcare Systems Hospitalization Hospitals In Vitro Industry Infrastructure Knowledge Lesion Levodopa Literature Machine Learning Modeling Mucositis Mus Neurons Parkinson Disease Pathogenesis Patient-Focused Outcomes Pharmaceutical Preparations Pharmacodynamics Pharmacologic Substance Pharmacology Phase Play Proteins Psychiatric therapeutic procedure Radio Reporting Rodent Rodent Model Role Safety Serious Adverse Event Standardization System Systems Biology Tardive Dyskinesia Testing Therapeutic Therapeutic Uses Tissues Validation adverse drug reaction base cancer therapy chemoradiation chemotherapy clinical development commercialization computational platform data pipeline drug development drug discovery improved in vitro testing in vivo metabolomics novel therapeutics off-patent pharmacokinetics and pharmacodynamics pharmacovigilance prevent programs response screening side effect success transcriptome sequencing transcriptomics

项目摘要

Project Summary Adverse drug reactions (ADRs), more commonly known as drug side effects, are estimated to cause over 200,000 deaths in the US annually, are responsible for 6.5% of all hospital admissions, and 28% of clinical trial failures. ADRs are estimated to increase healthcare costs by $136 billion per year in the USA alone. Current safety and modeling efforts that are commonly used in the pharma industry (such as PK/PD) do not elucidate the complex pathophysiology underlying ADRs. These safety and modeling approaches are used predominantly to quantitatively understand exposure-response relationships for clinical dosing, but with a few exceptions do not focus on the cellular pharmacodynamic mechanisms of why drugs cause ADRs. Elucidating the downstream and systemic effects of pharmaceuticals is critical to understanding ADR pathogenesis and developing safer therapies. Drugs can affect multiple proteins and each protein that they modulate may play roles in multiple cellular processes. Systems biology and bioinformatics approaches coupled with machine learning are crucial for understanding the multi-factorial pathophysiology of ADRs. In Phase I of this program, we developed an in vitro transcriptomics based computational platform that 1) predicts drug-side effect liability equivalent to current gold-standard approaches that require considerably more information about the compound and its effects, 2) defines genes that are relevant to ADR pathophysiology, and 3) identifies therapeutically beneficial compounds for the ADR. Based on the computational platform, we discovered a repurposing opportunity for an off-patent, non-FDA approved drug in Parkinson’s Disease that we are currently pursuing towards clinical development. This drug significantly improves levodopa’s efficacy, without exacerbating the drug’s major side effect which often precludes levodopa’s use. In Phase II of this proposal, we will continue to develop and expand the ADR computational platform. Further, we will hone our focus on two key clinically and commercially relevant ADRs: antipsychotic induced tardive dyskinesia and radio-/chemo- therapy induced mucosal inflammation. We will generate rich datasets for these ADRs to both validate our in vitro platform with in vivo data and to understand the pathophysiology of these ADRs at an unprecedented level. Further, we will use the datasets to generate computational predictions for discovering/repurposing drugs to improve safety in psychiatric and cancer treatments. The best predictions will be subsequently tested in vitro and developed through partnerships and external funding mechanisms.

项目概要药物不良反应 (ADR)，通常称为药物副作用，估计会导致过度美国每年有 20 万人死亡，占所有入院人数的 6.5%，占临床试验的 28% 据估计，仅在美国，ADR 每年就会增加 1,360 亿美元的医疗费用。制药行业常用的安全性和建模工作（例如 PK/PD）并未阐明使用这些安全性和建模方法来了解 ADR 背后的复杂病理生理学。主要是为了定量了解临床剂量的暴露-反应关系，但也有一些例外情况并不关注药物导致 ADR 的细胞药效机制。药物的下游和系统性影响对于了解 ADR 发病机制和开发更安全的疗法可以影响多种蛋白质以及它们调节的每种蛋白质。系统生物学和生物信息学方法与机器相结合在多种细胞过程中的作用。学习对于理解 ADR 的多因素病理生理学至关重要。我们开发了一个基于体外转录组学的计算平台，1）预测药物副作用的可能性相当于当前的黄金标准方法，需要更多关于化合物及其作用，2) 定义与 ADR 病理生理学相关的基因，以及 3) 确定基于计算平台，我们发现了一种对 ADR 有治疗作用的化合物。重新利用我们目前正在研究的一种治疗帕金森病的专利到期、未经 FDA 批准的药物的机会该药物正在进行临床开发，显着提高左旋多巴的疗效，而无需任何药物。加剧药物的主要副作用，这通常会阻碍左旋多巴的使用。我们将继续开发和扩展 ADR 计算平台，此外，我们将重点关注。两个关键的临床和商业相关的不良反应：抗精神病药引起的迟发性运动障碍和放射/化疗我们将为这些 ADR 生成丰富的数据集，以验证我们的研究成果。具有体内数据的体外平台，以前所未有的方式了解这些 ADR 的病理生理学此外，我们将使用数据集来生成用于发现/重新利用药物的计算预测。以提高精神科和癌症治疗的安全性，随后将在体外测试最佳预测。并通过伙伴关系和外部融资机制开发。