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

项目摘要 药物不良反应(ADRs)，通常被称为药物副作用，估计会导致 在美国，每年有20万人死亡，占所有住院人数的6.5%，临床试验的28% 失败。据估计，仅在美国，ADR每年就会增加1360亿美元的医疗成本。当前 制药行业中常用的安全性和建模工作(如PK/PD)并未阐明 ADRS背后复杂的病理生理学基础。使用这些安全和建模方法 主要是为了定量地了解临床剂量的暴露-反应关系，但也有少数 例外情况并没有集中在药物引起ADRs的细胞药效学机制上。澄清 药物的下游和全身效应对于理解ADR的发病机制和 开发更安全的治疗方法。药物可以影响多种蛋白质，它们调节的每一种蛋白质都可能发挥作用。 在多种细胞过程中扮演的角色。系统生物学和生物信息学与机器相结合的方法 学习对于理解ADRs的多因素病理生理学至关重要。在这个项目的第一阶段， 我们开发了一个基于体外转录组学的计算平台，可以1)预测药物副作用的易感性 相当于当前的黄金标准方法，这些方法需要更多关于 化合物及其作用，2)定义与ADR病理生理学相关的基因，3)识别 对ADR有治疗作用的化合物。基于计算平台，我们发现了一个 为我们目前治疗帕金森氏症的非专利、非FDA批准的药物提供重新用途的机会 追求临床发展。这种药物显著提高了左旋多巴的疗效，而没有 加剧了该药物的主要副作用，这通常会阻止左旋多巴的使用。在本提案的第二阶段， 我们将继续开发和扩大ADR计算平台。此外，我们将把重点放在 两个关键的临床和商业相关的不良反应：抗精神病药物引起的迟发性运动障碍和放化疗。 治疗引起粘膜炎症。我们将为这些ADR生成丰富的数据集，以验证我们的 利用体内数据和体外平台以前所未有的速度了解这些ADR的病理生理学 水平。此外，我们将使用数据集来生成发现/重新调整药物用途的计算预测 以提高精神科和癌症治疗的安全性。最好的预测随后将在体外进行测试。 并通过伙伴关系和外部筹资机制发展。

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.