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基于人细胞的药物安全性相关淘汰的表型分析

Human Cell-Based Phenotypic Profiling for Drug Safety-Related Attrition.

作者信息

Berg Ellen L

机构信息

Eurofins Discovery, Translational Biology, Burlingame, CA, United States.

出版信息

Front Big Data. 2019 Dec 11;2:47. doi: 10.3389/fdata.2019.00047. eCollection 2019.

Abstract

Ensuring the safety of new drugs is critically important to regulators, pharmaceutical researchers and patients alike. Even so, unexpected toxicities still account for 20-30% of clinical trial failures, in part due to the persistence of animal testing as the primary approach for de-risking new drugs. Clearly, improved methods for safety attrition that incorporate human-relevant biology are needed. This recognition has spurred interest in non-animal alternatives or new approach methodologies (NAMs) including models that utilize advances in the culture of human cell types to provide greater clinical relevance for assessing risk. These phenotypic assay systems use human primary and induced pluripotent stem cell-derived cells in various formats, including co-cultures and advanced cellular systems such as organoids, bioprinted tissues, and organs-on-a-chip. Despite the promise of these human-based phenotypic approaches, adoption of these platforms into drug discovery programs for reducing safety-related attrition has been slow. Here we discuss the value of large-scale human cell-based phenotypic profiling for incorporating human-specific biology into the de-risking process. We describe learnings from our experiences with human primary cell-based assays and analysis of clinically relevant reference datasets in developing -based toxicity signatures. We also describe how Adverse Outcome Pathway (AOP) frameworks can be used to integrate results from diverse platforms congruent with weight-of-evidence approaches from risk assessment to improve safety-related decisions in early discovery.

摘要

确保新药的安全性对监管机构、制药研究人员和患者都至关重要。即便如此,意外毒性仍占临床试验失败原因的20%-30%,部分原因是动物试验作为降低新药风险的主要方法仍然持续存在。显然,需要改进纳入人类相关生物学的安全性筛选方法。这种认识激发了人们对非动物替代方法或新方法学(NAMs)的兴趣,包括利用人类细胞类型培养进展的模型,以在评估风险时提供更高的临床相关性。这些表型分析系统以各种形式使用人类原代细胞和诱导多能干细胞衍生的细胞,包括共培养以及先进的细胞系统,如类器官、生物打印组织和芯片器官。尽管这些基于人类的表型方法前景广阔,但将这些平台应用于药物发现计划以减少与安全性相关的损耗进展缓慢。在此,我们讨论大规模基于人类细胞的表型分析在将人类特异性生物学纳入风险降低过程中的价值。我们描述了从基于人类原代细胞的检测经验以及在开发基于毒性特征的临床相关参考数据集分析中获得的经验教训。我们还描述了如何使用不良结局途径(AOP)框架来整合来自不同平台的结果,这些结果与风险评估中的证据权重方法相一致,以改善早期发现中与安全性相关的决策。

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